[PRE REVIEW]: PyBADS: Fast and robust black-box optimization in Python

[editorialbot]

Submitting author: !--author-handle-->@GurjeetSinghSangra<!--end-author-handle-- (Gurjeet Singh) Repository: https://github.com/acerbilab/pybads Branch with paper.md (empty if default branch): joss-submission Version: v1.0.0 Editor: !--editor-->@rkurchin<!--end-editor-- Reviewers: @max-little, @gaxler Managing EiC: Arfon Smith

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Software report:

github.com/AlDanial/cloc v 1.88  T=0.09 s (1242.6 files/s, 140514.2 lines/s)
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Language                     files          blank        comment           code
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Python                          59           1241           1574           5491
TeX                              2             77              0            633
INI                              4             14              0            376
reStructuredText                21            187             96            298
YAML                             6             24              5            210
Markdown                         5             75              0            173
Jupyter Notebook                 5              0           1209            140
TOML                             1              9              3             57
DOS Batch                        1             10              1             39
make                             1              6             11             18
CSS                              1              1              0              9
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SUM:                           106           1644           2899           7444
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gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 1580

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.48550/ARXIV.2303.09519 is OK
- 10.1145/3544489 is OK
- 10.23915/distill.00026 is OK
- 10.21105/joss.00024 is OK

MISSING DOIs

- 10.1007/s10898-012-9951-y may be a valid DOI for title: Derivative-free optimization: a review of algorithms and comparison of software implementations
- 10.1109/tte.2022.3218341 may be a valid DOI for title: Topology Comparison and Sensitivity Analysis of Fuel Cell Hybrid Systems for Electric Vehicles
- 10.1016/j.petsci.2022.06.016 may be a valid DOI for title: Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm
- 10.1101/2021.03.11.434913 may be a valid DOI for title: On the generality and cognitive basis of base-rate neglect
- 10.1016/j.jmapro.2021.02.052 may be a valid DOI for title: A novel energy partition model for belt grinding of Inconel 718
- 10.1016/j.jeem.2020.102317 may be a valid DOI for title: The impact of wildfires on the recreational value of heathland: A discrete factor approach with adjustment for on-site sampling
- 10.1137/040603371 may be a valid DOI for title: Mesh Adaptive Direct Search Algorithms for Constrained Optimization
- 10.1109/wsc.2006.323088 may be a valid DOI for title: Adaptation of the UOBYQA algorithm for noisy functions
- 10.1137/080716980 may be a valid DOI for title: OrthoMADS: A deterministic MADS instance with orthogonal directions
- 10.1007/s10589-020-00249-0 may be a valid DOI for title: Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates
- 10.1038/s41586-023-06124-2 may be a valid DOI for title: Expertise increases planning depth in human gameplay
- 10.1101/500413 may be a valid DOI for title: Causal inference in the multisensory brain
- 10.1038/s41593-019-0453-9 may be a valid DOI for title: Optimal policy for multi-alternative decisions
- 10.1038/s41562-019-0804-2 may be a valid DOI for title: Quantum reinforcement learning during human decision-making
- 10.1016/j.cub.2019.04.067 may be a valid DOI for title: Simple Acoustic Features Can Explain Phoneme-Based Predictions of Cortical Responses to Speech
- 10.1016/j.jmapro.2020.07.018 may be a valid DOI for title: A new method to achieve dynamic heat input monitoring in robotic belt grinding of Inconel 718
- 10.3390/en14030626 may be a valid DOI for title: Interpretation of Gas/Water Relative Permeability of Coal Using the Hybrid Bayesian-Assisted History Matching: New Insights
- 10.1109/jproc.2015.2494218 may be a valid DOI for title: Taking the Human Out of the Loop: A Review of Bayesian Optimization
- 10.1016/j.anucene.2020.108046 may be a valid DOI for title: Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release
- 10.1007/s40857-022-00277-2 may be a valid DOI for title: Application of Dual-Source Modal Dispersion and Variational Bayesian Monte Carlo Method for Local Geoacoustic Inversion in Weakly Range-Dependent Shallow Water
- 10.3390/pharmaceutics14040749 may be a valid DOI for title: Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling
- 10.1162/neco_a_01127 may be a valid DOI for title: Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions
- 10.1214/20-ba1200 may be a valid DOI for title: Parallel Gaussian Process Surrogate Bayesian Inference with Noisy Likelihood Evaluations
- 10.1016/0378-3758(91)90002-v may be a valid DOI for title: Bayes–Hermite quadrature

INVALID DOIs

- None

[editorialbot]

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

[GurjeetSinghSangra]

Potential Reviewers

From the given list, potential reviewers can be:

• max-little
• jungtaekkim
• vissarion
• salrm8
• WangKehan573
• madvn
• blakeaw

[GurjeetSinghSangra]

@editorialbot generate pdf

[editorialbot]

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

[GurjeetSinghSangra]

@editorialbot generate pdf

[editorialbot]

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

[rkurchin]

@editorialbot assign me as editor

[editorialbot]

Assigned! @rkurchin is now the editor

[rkurchin]

👋 @max-little, @vissarion, and/or @madvn, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

[max-little]

Yes, happy to take this on. Best Max

On Fri, 7 Jul 2023 at 21:46, Rachel Kurchin @.***> wrote:

👋 @max-little https://github.com/max-little, @vissarion https://github.com/vissarion, and/or @madvn https://github.com/madvn, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

— Reply to this email directly, view it on GitHub https://github.com/openjournals/joss-reviews/issues/5544#issuecomment-1626075899, or unsubscribe https://github.com/notifications/unsubscribe-auth/AYOOCSDFOTMAYNTDHHVSOF3XPBYRHANCNFSM6AAAAAAZCQKG7Y . You are receiving this because you were mentioned.Message ID: @.***>

-- Max Little (www.maxlittle.net) Associate Professor, University of Birmingham Turing Fellow, Alan Turing Institute TED Fellow (fellows.ted.com/profiles/max-little) Visiting Associate Professor, MIT Author: Machine Learning for Signal Processing, Oxford University Press global.oup.com/academic/product/machine-learning-for-signal-processing-9780198714934 Room 138, School of Computer Science University of Birmingham Birmingham B15 2TT UK +44 7710 609564 Skype: dr.max.little

[rkurchin]

@editorialbot add @max-little as reviewer

[editorialbot]

@max-little added to the reviewers list!

[vissarion]

@rkurchin thanks, I am a JOSS editor and unfortunately I have no time for this review.

[rkurchin]

👋 @arnavdas88, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

[arnavdas88]

👋 @arnavdas88, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

@rkurchin , sorry, this particular one looks a little out of my expertise... I need to pass on this one...

[rkurchin]

:wave: @rtbs-dev, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

[rkurchin]

👋 @sgbaird, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

[sgbaird]

@rkurchin hi, sorry, no bandwidth while preparing for Accelerate 2023. I like the black box constraint functionality of pybads though. Random fly-by comment is that it would be great to see the comparison to other optimizers highlighted on the main page (GitHub or rtd).

[rkurchin]

👋 @oesteban, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.htmla

[rkurchin]

👋 @gaxler, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.htmla

[gaxler]

Hi @rkurchin! Would love to review, the guidelines link seems to be broken. Could you please resend?

[rkurchin]

Haha, oops, I added an errant 'a' at the end, my mistake. This link should work: https://joss.readthedocs.io/en/latest/review_criteria.html

[madvn]

wave @max-little, @vissarion, and/or @madvn, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

Sorry, this is a little too far outside my wheelhouse. I don't feel like I could be helpful here.

[rkurchin]

@editorialbot add @gaxler as reviewer

[editorialbot]

@gaxler added to the reviewers list!

[rkurchin]

@editorialbot start review

[editorialbot]

OK, I've started the review over in https://github.com/openjournals/joss-reviews/issues/5694.