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20231009T120458-bc80038991f97e11b8b3d6fb8bd48380eee160b5 + 20231009120458 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 10 + 2023 + + + 8 + + 90 + + + + BayesO: A Bayesian optimization framework in +Python + + + + Jungtaek + Kim + https://orcid.org/0000-0002-1905-1399 + + + Seungjin + Choi + https://orcid.org/0000-0002-7873-4616 + + + + 10 + 09 + 2023 + + + 5320 + + + 10.21105/joss.05320 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.8419023 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5320 + + + + 10.21105/joss.05320 + https://joss.theoj.org/papers/10.21105/joss.05320 + + + https://joss.theoj.org/papers/10.21105/joss.05320.pdf + + + + + + A tutorial on Bayesian optimization of +expensive cost 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On the +likelihood that one unknown probability exceeds another in view of the +evidence of two samples. Biometrika, 25(3/4), 285–294. +https://doi.org/10.1093/biomet/25.3-4.285 + + + Global optimization of stochastic black-box +systems via sequential kriging meta-models + Huang + Journal of Global +Optimization + 34 + 10.1007/s10898-005-2454-3 + 2006 + Huang, D., Allen, T. T., Notz, W. I., +& Zheng, N. (2006). Global optimization of stochastic black-box +systems via sequential kriging meta-models. Journal of Global +Optimization, 34, 441–466. +https://doi.org/10.1007/s10898-005-2454-3 + + + Gaussian process optimization in the bandit +setting: No regret and experimental design + Srinivas + Proceedings of the international conference +on machine learning (ICML) + 2010 + Srinivas, N., Krause, A., Kakade, S., +& Seeger, M. (2010). Gaussian process optimization in the bandit +setting: No regret and experimental design. Proceedings of the +International Conference on Machine Learning (ICML), +1015–1022. + + + A limited memory algorithm for bound +constrained optimization + Byrd + SIAM Journal on Scientific +Computing + 5 + 16 + 10.1137/0916069 + 1995 + Byrd, R. H., Lu, P., Nocedal, J., +& Zhu, C. (1995). A limited memory algorithm for bound constrained +optimization. SIAM Journal on Scientific Computing, 16(5), 1190–1208. +https://doi.org/10.1137/0916069 + + + Convergence properties of evolution +strategies with the derandomized covariance matrix adaptation: The +(\mu/\mu_\mathrm{I}, \lambda)-CMA-ES + Hansen + Proceedings of the european congress on +intelligent techniques and soft computing (EUFIT) + 1997 + Hansen, N., & Ostermeier, A. +(1997). Convergence properties of evolution strategies with the +derandomized covariance matrix adaptation: The (\mu/\mu_\mathrm{I}, +\lambda)-CMA-ES. Proceedings of the European Congress on Intelligent +Techniques and Soft Computing (EUFIT), 650–654. + + + Lipschitzian optimization without the +Lipschitz constant + Jones + Journal of Optimization Theory and +Applications + 1 + 79 + 10.1007/BF00941892 + 1993 + Jones, D. R., Perttunen, C. D., & +Stuckman, B. E. (1993). Lipschitzian optimization without the Lipschitz +constant. Journal of Optimization Theory and Applications, 79(1), +157–181. https://doi.org/10.1007/BF00941892 + + + Quasi-Monte Carlo methods in +Python + Roy + Journal of Open Source +Software + 84 + 8 + 10.21105/joss.05309 + 2023 + Roy, P. T., Owen, A. B., Balandat, +M., & Haberland, M. (2023). Quasi-Monte Carlo methods in Python. +Journal of Open Source Software, 8(84), 5309. +https://doi.org/10.21105/joss.05309 + + + tqdm + The tqdm authors + 2016 + The tqdm authors. (2016). tqdm. +GitHub. https://github.com/tqdm/tqdm + + + GPy: A Gaussian process framework in +Python + GPy + 2012 + GPy. (2012). GPy: A Gaussian process +framework in Python. GitHub. +https://github.com/SheffieldML/GPy + + + GPyOpt: A Bayesian optimization framework in +Python + The GPyOpt authors + 2016 + The GPyOpt authors. (2016). GPyOpt: A +Bayesian optimization framework in Python. GitHub. +https://github.com/SheffieldML/GPyOpt + + + SMAC3: A versatile Bayesian optimization +package for hyperparameter optimization + Lindauer + Journal of Machine Learning +Research + 54 + 23 + 2022 + Lindauer, M., Eggensperger, K., +Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhkopf, T., Sass, +R., & Hutter, F. (2022). SMAC3: A versatile Bayesian optimization +package for hyperparameter optimization. Journal of Machine Learning +Research, 23(54), 1–9. + + + PyTorch: An imperative style, +high-performance deep learning library + Paszke + Advances in neural information processing +systems (NeurIPS) + 32 + 2019 + Paszke, A., Gross, S., Massa, F., +Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, +N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, +M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S. +(2019). PyTorch: An imperative style, high-performance deep learning +library. Advances in Neural Information Processing Systems (NeurIPS), +32, 8026–8037. + + + TensorFlow: A system for large-scale machine +learning + Abadi + USENIX symposium on operating systems design +and implementation (OSDI) + 2016 + Abadi, M., Barham, P., Chen, J., +Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., +Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. +G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., … Zheng, X. +(2016). TensorFlow: A system for large-scale machine learning. USENIX +Symposium on Operating Systems Design and Implementation (OSDI), +265–283. + + + GPflowOpt: A Bayesian optimization library +using TensorFlow + Knudde + arXiv preprint +arXiv:1711.03845 + 2017 + Knudde, N., van der Herten, J., +Dhaene, T., & Couckuyt, I. (2017). GPflowOpt: A Bayesian +optimization library using TensorFlow. arXiv Preprint +arXiv:1711.03845. + + + BoTorch: A framework for efficient +Monte-Carlo Bayesian optimization + Balandat + Advances in neural information processing +systems (NeurIPS) + 33 + 2020 + Balandat, M., Karrer, B., Jiang, D. +R., Daulton, S., Letham, B., Wilson, A. G., & Bakshy, E. (2020). +BoTorch: A framework for efficient Monte-Carlo Bayesian optimization. +Advances in Neural Information Processing Systems (NeurIPS), 33, +21524–21538. + + + A new method of locating the maximum point of +an arbitrary multipeak curve in the presence of noise + Kushner + Journal of Basic Engineering + 1 + 86 + 10.1115/1.3653121 + 1964 + Kushner, H. J. (1964). A new method +of locating the maximum point of an arbitrary multipeak curve in the +presence of noise. Journal of Basic Engineering, 86(1), 97–106. +https://doi.org/10.1115/1.3653121 + + + + + + From 39072981bdf13535cea1b82e83164e12bf20d5b5 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Mon, 9 Oct 2023 13:05:28 +0100 Subject: [PATCH 3/5] Creating 10.21105.joss.05320.jats --- joss.05320/10.21105.joss.05320.jats | 1080 +++++++++++++++++++++++++++ 1 file changed, 1080 insertions(+) create mode 100644 joss.05320/10.21105.joss.05320.jats diff --git a/joss.05320/10.21105.joss.05320.jats b/joss.05320/10.21105.joss.05320.jats new file mode 100644 index 0000000000..1ad2b8cba8 --- /dev/null +++ b/joss.05320/10.21105.joss.05320.jats @@ -0,0 +1,1080 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5320 +10.21105/joss.05320 + +BayesO: A Bayesian optimization framework in +Python + + + +https://orcid.org/0000-0002-1905-1399 + +Kim +Jungtaek + + + + +https://orcid.org/0000-0002-7873-4616 + +Choi +Seungjin + + + + + +University of Pittsburgh, USA + + + + +Intellicode, South Korea + + + + +6 +12 +2022 + +8 +90 +5320 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Bayesian optimization +global optimization +black-box optimization +optimization + + + + + + Summary +

Bayesian optimization is a sample-efficient method for solving the + optimization of a black-box function. In particular, it successfully + shows its effectiveness in diverse applications such as hyperparameter + optimization, automated machine learning, material design, sequential + assembly, and chemical reaction optimization. In this paper we present + an easy-to-use Bayesian optimization framework, referred to as + BayesO, which is written in Python and licensed under + the MIT license. To briefly introduce our software, we describe the + functionality of BayesO and various components for software + development.

+
+ + Statement of need +

Bayesian optimization + (Brochu + et al., 2010; + Garnett, + 2023; + Shahriari + et al., 2016) is a sample-efficient method for solving the + optimization of a black-box function + + f: + + + 𝐱=argmin𝐱𝒳f(𝐱)or𝐱=argmax𝐱𝒳f(𝐱), + where + + 𝒳d + is a + + d-dimensional + space. In general, finding a solution + + 𝐱 + of Equation 1, + i.e., a global optimum on + + 𝒳, + is time-consuming since we cannot employ any knowledge, e.g., + gradients and Hessians, in solving this problem. Compared to other + possible approaches, e.g., random search and evolutionary algorithm, + Bayesian optimization successfully shows its effectiveness by + utilizing a probabilistic regression model and an acquisition + function. In particular, the sample-efficient approach of our interest + enables us to apply it in various real-world applications such as + hyperparameter optimization + (Snoek + et al., 2012), automated machine learning + (Feurer + et al., 2015, + 2022), + neural architecture search + (Kandasamy + et al., 2018), material design + (Frazier + & Wang, 2016), free-electron laser configuration search + (Duris + et al., 2020), organic molecule synthesis + (Korovina + et al., 2020), sequential assembly + (Kim + et al., 2020), and chemical reaction optimization + (Shields + et al., 2021).

+

In this paper, we present an easy-to-use Bayesian optimization + framework, referred to as BayesO (pronounced + “bayes-o”), to effortlessly utilize Bayesian optimization in the + problems of interest to practitioners. Our BayesO is written in one of + the most popular programming languages, Python, and licensed under the + MIT license. Moreover, it provides various features including + different types of input variables (e.g., vectors and sets + (Kim + et al., 2021)) and different surrogate models (e.g., Gaussian + process regression + (Rasmussen + & Williams, 2006) and + Student- + + t + process regression + (Shah + et al., 2014)). Along with the description of such + functionality, we cover various components for software development in + the BayesO project. We hope that this BayesO project encourages + researchers and practitioners to readily utilize the powerful + black-box optimization technique in diverse academic and industrial + fields.

+ +

Visualization of Bayesian optimization procedure. Given + an objective function, + Equation 2 + (colored by turquoise) and four initial points (denoted as light + blue + + + + at iteration 1), a query point (denoted as pink + + + 𝚡) + is determined by constructing a surrogate model (colored by orange) + and maximizing an acquisition function (colored by light green) + every + iteration.

+ +
+
+ + Bayesian optimization +

As discussed in the work + (Brochu + et al., 2010; + Garnett, + 2023; + Shahriari + et al., 2016), supposing that a target objective function is + black-box, Bayesian optimization is an approach to optimizing the + objective in a sample-efficient manner. It repeats three primary + steps:

+ + +

Building a probabilistic regression model, which is capable of + estimating the degrees of exploration and exploitation;

+
+ +

Optimizing an acquisition function, which is defined with the + probabilistic regression model;

+
+ +

Evaluating the target objective at a query point, which is + determined by optimizing the acquisition function,

+
+
+

until a predefined stopping criterion, e.g., an iteration budget or + a budget of wall-clock time, is satisfied. Eventually, the best + solution among the queries evaluated is selected by considering the + function evaluations. As shown in + [fig:bo_steps], + Bayesian optimization iteratively finds a candidate of global optimum, + repeating the aforementioned steps. Note that, for this example, an + objective function is + + + f(x)=2sin(x)+2cos(2x)+0.05x, + where + + x[5,5], + and Gaussian process regression and expected improvement are used as a + surrogate model and an acquisition function, respectively.

+

Several related projects on Bayesian optimization have been + proposed. GPyOpt + (The + GPyOpt authors, 2016), built on GPy + (GPy, + 2012), has been implemented. SMAC3 + (Lindauer + et al., 2022) has been developed using random forests + (Breiman, + 2001). Moreover, by making use of modern automatic + differentiation tools, i.e., TensorFlow + (Abadi + et al., 2016) and PyTorch + (Paszke + et al., 2019), GPflowOpt + (Knudde + et al., 2017) and BoTorch + (Balandat + et al., 2020) have been introduced.

+
+ + Overview of BayesO + +

Logo of + BayesO

+ +
+

In this section we cover the overview of BayesO including + probabilistic regression models and acquisition functions, by + referring to BayesO v0.5.4. For higher versions of BayesO, see + official documentation. Note that the BayesO logo is shown in + [fig:logo_bayeso].

+

BayesO supports the following probabilistic regression models:

+ + +

Gaussian process regression + (Rasmussen + & Williams, 2006);

+
+ +

Student- + + t + process regression + (Shah + et al., 2014);

+
+ +

Random forest regression + (Breiman, + 2001).

+
+
+

Although random forest regression is not a probabilistic model + inherently, we can compute its mean and variance functions as reported + by Hutter et al. + (2014).

+

We implement several acquisition functions:

+ + +

pure exploration;

+
+ +

pure exploitation;

+
+ +

probability of improvement + (Kushner, + 1964);

+
+ +

expected improvement + (Jones + et al., 1998);

+
+ +

augmented expected improvement + (Huang + et al., 2006);

+
+ +

Gaussian process upper confidence bound + (Srinivas + et al., 2010).

+
+
+

As a strategy to optimize an acquisition function, we suggest the + following options: DIRECT + (Jones + et al., 1993), CMA-ES + (Hansen + & Ostermeier, 1997), and L-BFGS-B + (Byrd + et al., 1995). In addition, we also include Thompson sampling + (Thompson, + 1933) in BayesO.

+

Furthermore, to support an easy-to-use interface, we implement the + wrappers of Bayesian optimization for particular scenarios:

+ + +

a run with randomly-chosen initial inputs;

+
+ +

a run with initial inputs provided;

+
+ +

a run with initial inputs provided and their evaluations.

+
+
+
+ + Software development for BayesO +

To manage BayesO productively, we actively utilize external + development management packages.

+ + +

Code analysis: Our software is monitored and inspected to + satisfy the code conventions predefined in our software.

+
+ +

Type hints: As supported in Python 3, we provide type hints for + any arguments.

+
+ +

Unit tests: Unit tests for our software are included. We have + achieved 100% coverage. In addition, unit tests for measuring + execution time are also provided.

+
+ +

Dependency: Our package depends on NumPy + (Harris + et al., 2020), SciPy + (Virtanen + et al., 2020), a quasi-Monte Carlo submodule in SciPy + (Roy + et al., 2023), pycma + (Hansen + et al., 2019), and tqdm + (The + tqdm authors, 2016).

+
+ +

Installation: Our software is released via the Python Package + Index (PyPI) meaning users can easily install BayesO into their + environment.

+
+ +

Documentation: We create official documentation with + docstring.

+
+
+
+ + Conclusion +

In this work we have presented a Bayesian optimization framework, + named BayesO. We hope that our project enables many researchers to + suggest a new algorithm by modifying BayesO and many practitioners to + utilize Bayesian optimization in their applications.

+
+ + Acknowledgements +

The BayesO project has been started when JK and SC were with + POSTECH, and it has been mostly developed at POSTECH.

+
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