[REVIEW]: quantile-forest: A Python Package for Quantile Regression Forests

[editorialbot]

Submitting author: @reidjohnson (Reid A Johnson)
Repository: https://github.com/zillow/quantile-forest
Branch with paper.md (empty if default branch):
Version: v1.2.4
Editor: @jbytecode
Reviewers: @jncraton, @oparisot
Archive: 10.5281/zenodo.10521419

Status

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Markdown: [![status](https://joss.theoj.org/papers/443b42ff651cb0898214cb253a566ce6/status.svg)](https://joss.theoj.org/papers/443b42ff651cb0898214cb253a566ce6)

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Reviewer instructions & questions

@jncraton & @salrm8 & @astrogilda, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review.
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✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

📝 Checklist for @jncraton

📝 Checklist for @oparisot

[editorialbot]

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.88  T=0.08 s (461.6 files/s, 75230.0 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          13            699            918           2302
Cython                           2            201            274            651
TeX                              2             21              0            214
reStructuredText                 8            112             50            187
YAML                             5             36              8            162
Markdown                         2             42              0             97
JavaScript                       1              3             10             50
CSS                              1             11              3             45
DOS Batch                        1              9              1             32
TOML                             1              1              1             26
make                             1              4              7              9
HTML                             1              0              0              7
-------------------------------------------------------------------------------
SUM:                            38           1139           1272           3782
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 1156

[editorialbot]

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

OK DOIs

- 10.1214/18-aos1709 is OK
- 10.1109/mcse.2010.118 is OK
- 10.1007/s11749-016-0481-7 is OK
- 10.1023/A:1010933404324 is OK
- 10.1016/j.jenvman.2021.112509 is OK
- 10.1007/s10729-022-09609-0 is OK
- 10.1371/journal.pone.0205155 is OK
- 10.1002/hyp.7110 is OK
- 10.1155/2020/1972962 is OK
- 10.7717/peerj.5518 is OK
- 10.1017/CBO9780511754098 is OK
- 10.1007/978-3-319-33383-0_5 is OK
- 10.3390/en14010158 is OK
- 10.1016/j.ijforecast.2021.11.001 is OK
- 10.1080/01621459.2017.1319839 is OK
- 10.18637/jss.v077.i01 is OK
- 10.1016/j.energy.2018.07.019 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[jbytecode]

@jncraton, @salrm8, @astrogilda

Thank you for accepting our invitation to review for JOSS.

This is the review thread. Firstly, type

@editorialbot generate my checklist

to generate your own checklist. In that checklist, there are many check items. Whenever you complete the corresponding task, you can check them off.

Please write your comments as separate posts and do not modify your checklist descriptions.

The review process is interactive so you can always interact with the authors, reviewers, and the editor. You can also create issues and pull requests in the target repository. Please do mention this thread's URL in the issues so we can keep tracking what is going on out of our world.

Please do not hesitate to ask me about anything at anytime.

Thank you in advance!

[jncraton]

Review checklist for @jncraton

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the https://github.com/zillow/quantile-forest?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@reidjohnson) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

[jbytecode]

@salrm8, @astrogilda - Could you please create your review checklist and update your status? Thank you in advance!

[jncraton]

@reidjohnson I have a question about how this compares to other Python packages available. In the paper, you mention other implementations in C++ and R, but I didn't see comparisons to other Python packages. Here's a quote from the paper:

The absence of a comprehensive Python implementation of the QRF algorithm severely hampers researchers’ ability to utilize and benefit from its wide-ranging applications. We seek to fill this need by providing a comprehensive Python-based implementation of the QRF algorithm. The QRF implementation provided in this package has been optimized for training and inference speed

There may be benefit in mentioning that other packages exist and explicitly stating how your package adds value in terms of factors like performance, maintenance status, robustness, ease of use, etc.

I'm not an expert in this area, but a quick search turned up the following Python packages that also appear to offer similar functionality:

1. https://pypi.org/project/scikit-garden/
2. https://pypi.org/project/sklearn-quantile/

[reidjohnson]

@jncraton Thanks for the suggestions here. You're correct to identify these packages as viable alternatives, and I'll update the paper to reference them accordingly. As it stands, the primary differentiators between this package and those are:

• Maintenance: One of the packages (scikit-garden) is presently unmaintained in incompatible with recent versions of Cython and scikit-learn. (Nit: This package is also presently the only one to provide prebuilt wheels for Python 3.12.)
• Performance: On large-scale data, the runtime gap between this package and the others becomes orders of magnitude. For example, fitting and predicting on the the toy dataset X, y = datasets.make_regression(n_samples=1_000_000, n_features=10) with comparable parameterizations takes about 3 hours for this package on a standard Macbook, but at least several days for the other packages. I don't necessarily want to do a performance bake-off, but I can provide a more thorough runtime comparison if desired or deemed useful.
• Convenience: There are some crucial differences in the way arguments are specified in the packages, and other relevant functionality. For example, in this package, quantiles can be specified at prediction time (in contrast to sklearn-quantile), and this package provides other convenience methods like out-of-bag scoring, quantile rank calculations, and proximity/similarity estimation.

So, given all that, I'm happy to iterate on the relevant parts of the paper until there's agreement. Here's an initial proposed revision of the paragraph:

We seek to fill this need by providing a comprehensive Python-based implementation of the QRF algorithm. While other Python-based QRF implementations exist and are meaningful additions to the Python ecosystem [@scikitgarden2017;@sklearnquantile2022], none currently provide performance and functionality comparable to the implementations available in R, such as specifying quantiles at inference time or scaling to large datasets without approximation. By contrast, the QRF implementation provided in this package has been optimized for training and inference speed, enabling it to scale to millions of samples. It also allows specifying prediction quantiles after training, permitting a trained model to be reused to estimate conditional quantiles as needed. Beyond this, the package includes utilities that enhance the algorithm's applicability and usefulness for researchers and practitioners. These utilities include:

[jncraton]

@reidjohnson That looks great to me! I appreciate both the tone and the content that you've landed on in that update.

I don't personally see a strong need for a full performance bake-off, but it might be helpful to quantify the performance benefits somewhere, even if this is as simple as stating that this takes runtime from days to hours on a test set of 1 million samples versus prior less optimized approaches.

The only box I'm having difficulty checking off at this point is the community guidelines:

Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

I see that you have a CONTRIBUTING file that includes helpful info for developers, but not everything above is stately clearly and explicitly.

[jbytecode]

@jncraton, @reidjohnson - thank you all for the smooth reviewing process.

@salrm8, @astrogilda - may I kindly ask you to create your task lists and start your reviews?

Thank you in advance!

[jbytecode]

@salrm8, @astrogilda - Could you please update your status and inform us on how is your review going? Thank you in advance.

Note: It seems you haven't even created your checklist. Could you please at least give a life signal and start your review? Thank you.

[reidjohnson]

@jncraton: Thank you for your thoughtful suggestions; I've updated the paper and contributing guidelines accordingly.
@jbytecode: Thank you for kindly nudging things along here.

[jbytecode]

@salrm8, @astrogilda - Could you please update your status and inform us on how is your review going? Thank you in advance.

It would be nice to at least get a sign of life from you.

Note: It seems you haven't even created your checklist. Could you please a start your review? Thank you.

[jbytecode]

@salrm8, @astrogilda - Hello there! In the pre-review issue, I noticed that you were slated for a review in December 2023. As we're now entering the first half of the month, I kindly request that you provide a brief update or initiate the review process. If this isn't feasible, please inform me so I can explore alternative reviewers. Thank you in advance for your attention to this matter!

[jbytecode]

@oparisot - Two of our reviewers haven't responded to our reminders. We need a second reviewer for this submission. Are you still available to review this for us?

[jbytecode]

@editorialbot remove @salrm8 from reviewers

[editorialbot]

@salrm8 removed from the reviewers list!

[jbytecode]

@editorialbot remove @astrogilda from reviewers

[editorialbot]

@astrogilda removed from the reviewers list!

[oparisot]

Hi! Ok for me! Olivier Le mar. 2 janv. 2024, 16:49, Mehmet Hakan Satman ***@***.***> a écrit :

…

@oparisot <https://github.com/oparisot> - Two of our reviewers haven't responded to our reminders. We need a second reviewer for this submission. Are you still available to review this for us? — Reply to this email directly, view it on GitHub <#5976 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEHLM3WX6Z5BOKBLEE4ON33YMQT7RAVCNFSM6AAAAAA6MGEJDWVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQNZUGE4TSMJVGQ> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[jbytecode]

@editorialbot generate pdf

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[jbytecode]

@reidjohnson - Everything seems good to me. Please have a full read of the paper one more time. Please correct any issue if exists. Please ping me when you've done with them.

[reidjohnson]

@editorialbot generate pdf

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[reidjohnson]

@jbytecode: I made some minor corrections/updates to the paper. No claims, statements, or references were changed (full diff here). With that, the paper looks good to me.

[jbytecode]

@editorialbot check references

[editorialbot]

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

OK DOIs

- 10.1214/18-aos1709 is OK
- 10.1109/mcse.2010.118 is OK
- 10.1007/s11749-016-0481-7 is OK
- 10.1023/A:1010933404324 is OK
- 10.1016/j.jenvman.2021.112509 is OK
- 10.1007/s10729-022-09609-0 is OK
- 10.1371/journal.pone.0205155 is OK
- 10.1002/hyp.7110 is OK
- 10.1155/2020/1972962 is OK
- 10.7717/peerj.5518 is OK
- 10.1017/CBO9780511754098 is OK
- 10.1007/978-3-319-33383-0_5 is OK
- 10.3390/en14010158 is OK
- 10.1016/j.ijforecast.2021.11.001 is OK
- 10.1080/01621459.2017.1319839 is OK
- 10.18637/jss.v077.i01 is OK
- 10.1016/j.energy.2018.07.019 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[jbytecode]

@editorialbot generate pdf

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[jbytecode]

@editorialbot recommend-accept

[editorialbot]

Attempting dry run of processing paper acceptance...

[editorialbot]

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

OK DOIs

- 10.1214/18-aos1709 is OK
- 10.1109/mcse.2010.118 is OK
- 10.1007/s11749-016-0481-7 is OK
- 10.1023/A:1010933404324 is OK
- 10.1016/j.jenvman.2021.112509 is OK
- 10.1007/s10729-022-09609-0 is OK
- 10.1371/journal.pone.0205155 is OK
- 10.1002/hyp.7110 is OK
- 10.1155/2020/1972962 is OK
- 10.7717/peerj.5518 is OK
- 10.1017/CBO9780511754098 is OK
- 10.1007/978-3-319-33383-0_5 is OK
- 10.3390/en14010158 is OK
- 10.1016/j.ijforecast.2021.11.001 is OK
- 10.1080/01621459.2017.1319839 is OK
- 10.18637/jss.v077.i01 is OK
- 10.1016/j.energy.2018.07.019 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[editorialbot]

👋 @openjournals/dsais-eics, this paper is ready to be accepted and published.

Check final proof 👉📄 Download article

If the paper PDF and the deposit XML files look good in openjournals/joss-papers#4923, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

[jbytecode]

@arfon - This submission is ready to go!

[arfon]

@editorialbot accept

[editorialbot]

Doing it live! Attempting automated processing of paper acceptance...

[editorialbot]

Ensure proper citation by uploading a plain text CITATION.cff file to the default branch of your repository.

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CITATION.cff

cff-version: "1.2.0"
authors:
- family-names: Johnson
  given-names: Reid A.
  orcid: "https://orcid.org/0009-0001-1449-4940"
doi: 10.5281/zenodo.10521419
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Johnson
    given-names: Reid A.
    orcid: "https://orcid.org/0009-0001-1449-4940"
  date-published: 2024-01-19
  doi: 10.21105/joss.05976
  issn: 2475-9066
  issue: 93
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 5976
  title: "quantile-forest: A Python Package for Quantile Regression
    Forests"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.05976"
  volume: 9
title: "quantile-forest: A Python Package for Quantile Regression
  Forests"

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[editorialbot]

🐘🐘🐘 👉 Toot for this paper 👈 🐘🐘🐘

[editorialbot]

🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSS! 🚨🚨🚨

Here's what you must now do:

0. Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.05976 joss-papers#4926
1. Wait five minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.05976
2. If everything looks good, then close this review issue.
3. Party like you just published a paper! 🎉🌈🦄💃👻🤘

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[arfon]

@jncraton, @oparisot – many thanks for your reviews here and to @jbytecode for editing this submission! JOSS relies upon the volunteer effort of people like you and we simply wouldn't be able to do this without you ✨

@reidjohnson – your paper is now accepted and published in JOSS ⚡🚀💥

[editorialbot]

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[reidjohnson]

Wonderful! @jbytecode, @jncraton, @oparisot: Thank you for taking the time to edit and review this submission, I greatly appreciate your efforts.