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

[editorialbot]

Submitting author: !--author-handle-->@reidjohnson<!--end-author-handle-- (Reid A Johnson) Repository: https://github.com/zillow/quantile-forest Branch with paper.md (empty if default branch): Version: v1.2.0 Editor: !--editor-->@jbytecode<!--end-editor-- Reviewers: @jncraton, @salrm8, @astrogilda Managing EiC: George K. Thiruvathukal

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Thanks for submitting your paper to JOSS @reidjohnson. Currently, there isn't a JOSS editor assigned to your paper.

@reidjohnson if you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). You can search the list of people that have already agreed to review and may be suitable for this submission.

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[editorialbot]

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.88  T=0.06 s (633.5 files/s, 100748.0 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          12            641            889           1982
Cython                           2            198            272            650
TeX                              2             20              0            182
reStructuredText                 7            108             48            179
YAML                             4             35              4            155
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
HTML                             2              1              0             10
make                             1              4              7              9
-------------------------------------------------------------------------------
SUM:                            36           1073           1235           3417
-------------------------------------------------------------------------------

gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 1199

[editorialbot]

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

OK DOIs

- None

MISSING DOIs

- 10.1214/18-aos1709 may be a valid DOI for title: Generalized random forests
- 10.1109/mcse.2010.118 may be a valid DOI for title: Cython: The best of both worlds
- 10.1007/s11749-016-0481-7 may be a valid DOI for title: A random forest guided tour
- 10.1007/978-3-030-56485-8_3 may be a valid DOI for title: Random forests
- 10.1016/j.jenvman.2021.112509 may be a valid DOI for title: A spatially based quantile regression forest model for mapping rural land values
- 10.1007/s10729-022-09609-0 may be a valid DOI for title: Quantile regression forests for individualized surgery scheduling
- 10.1371/journal.pone.0205155 may be a valid DOI for title: A quantile regression forest based method to predict drug response and assess prediction reliability
- 10.1002/hyp.7110 may be a valid DOI for title: Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests
- 10.1155/2020/1972962 may be a valid DOI for title: Long-term exchange rate probability density forecasting using Gaussian kernel and quantile random forest
- 10.3390/en14010158 may be a valid DOI for title: Probabilistic forecasting of wind turbine icing related production losses using quantile regression forests
- 10.1080/01621459.2017.1319839 may be a valid DOI for title: Estimation and inference of heterogeneous treatment effects using random forests
- 10.1016/j.energy.2018.07.019 may be a valid DOI for title: Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination

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:

[arfon]

@reidjohnson – due to the relatively small size of this code, the editors will now discuss if it meets the substantial scholarly effort criterion for review by JOSS. We should get back to you sometime next week. If you want to fix the DOIs (noting that @editorialbot's suggestions are not always right), you can, then use the following commands (one at a time, as the first line of a new comment) to regenerate the PDF and check the references.

@editorialbot generate pdf @editorialbot check references

[arfon]

@editorialbot query scope

[editorialbot]

Submission flagged for editorial review.

[reidjohnson]

@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:

[reidjohnson]

@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
- 10.1016/j.energy.2018.07.019 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[arfon]

@editorialbot generate pdf

[arfon]

@editorialbot commands

[editorialbot]

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[arfon]

@editorialbot generate pdf

[arfon]

@editorialbot invite @olexandr-konovalov as editor

:wave: @olexandr-konovalov – would you be willing to take on this submission for us?

[editorialbot]

Invitation to edit this submission sent!

[arfon]

Friendly reminder here @olexandr-konovalov – might you be able to take this submission on?

[arfon]

@editorialbot invite @jbytecode as editor

:wave: @jbytecode – I know you've just wrapped up a submission, and I wondered if you'd be willing to take this one on for us?

[editorialbot]

Invitation to edit this submission sent!

[jbytecode]

@editorialbot assign me as editor

@arfon - sure, thank you for inviting me.

[editorialbot]

Assigned! @jbytecode is now the editor

[jbytecode]

@arfon - Can we trigger the AI-based automatic reviewer recommendations with similarities command here? I think it is automatically called in recently opened issues but this issue has been created before the tool was implemented.

[arfon]

@jbytecode – we can. But for now, it happens after the pdf generation (I'll break it out into its own step/command :soon:)

[arfon]

@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:

[editorialbot]

Five most similar historical JOSS papers:

Confidence Intervals for Random Forests in Python Submitting author: @kpolimis Handling editor: @jakevdp (Retired) Reviewers: @DannyArends Similarity score: 0.8313

UQit: A Python package for uncertainty quantification (UQ) in computational fluid dynamics (CFD) Submitting author: @salrm8 Handling editor: @drvinceknight (Active) Reviewers: @jayten, @ctdegroot Similarity score: 0.8254

edarf: Exploratory Data Analysis using Random Forests Submitting author: @zmjones Handling editor: @cMadan (Active) Reviewers: @PhilippPro Similarity score: 0.8238

RandomForestsGLS: An R package for Random Forests for dependent data Submitting author: @ArkajyotiSaha Handling editor: @fabian-s (Active) Reviewers: @mnwright, @pdwaggoner Similarity score: 0.8175

aorsf: An R package for supervised learning using the oblique random survival forest Submitting author: @bcjaeger Handling editor: @danielskatz (Active) Reviewers: @danielskatz Similarity score: 0.8127

⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before before considering asking the reviewers of these papers to review again for JOSS.

[jbytecode]

👋👋👋 Dear @kpolimis, @salrm8, @zmjones, @ArkajyotiSaha, @bcjaeger 👋👋👋

Would you be willing to assist in reviewing this submission for JOSS (Journal of Open Source Software)?

JOSS publishes articles about open source research software. The submission I'd like you to review is titled:

quantile-forest: A Python Package for Quantile Regression Forests

You can find more information at the top of this Github issue (https://github.com/openjournals/joss-reviews/issues/5795).

The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. If you have any questions please let me know.

This is the pre-review issue. After setting at least 2 reviewers we will start the review process in a separate thread. In that thread, there will be 23 check items for each single reviewer.

Thank you in advance!

[bcjaeger]

Hello! I will decline this time since I don't use Python and I need to be out of town over the next few weeks, but thank you for the invitation.

[ArkajyotiSaha]

Hello! Thanks so much for the invitation. Unfortunately, I will have to decline, since I have very limited experience with Python.

-Arka

On Mon, Oct 16, 2023 at 12:32 AM Mehmet Hakan Satman < @.***> wrote:

👋👋👋 Dear @kpolimis https://github.com/kpolimis, @salrm8 https://github.com/salrm8, @zmjones https://github.com/zmjones, @ArkajyotiSaha https://github.com/ArkajyotiSaha, @bcjaeger https://github.com/bcjaeger 👋👋👋

Would you be willing to assist in reviewing this submission for JOSS (Journal of Open Source Software)?

JOSS publishes articles about open source research software. The submission I'd like you to review is titled:

quantile-forest: A Python Package for Quantile Regression Forests

You can find more information at the top of this Github issue (#5795 https://github.com/openjournals/joss-reviews/issues/5795).

The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. If you have any questions please let me know.

This is the pre-review issue. After setting at least 2 reviewers we will start the review process in a separate thread. In that thread, there will be 23 check items for each single reviewer.

Thank you in advance!

— Reply to this email directly, view it on GitHub https://github.com/openjournals/joss-reviews/issues/5795#issuecomment-1763891596, or unsubscribe https://github.com/notifications/unsubscribe-auth/AF3LKOWJ4CNH2RFBTWV4OX3X7TPI5ANCNFSM6AAAAAA4IJ53XQ . You are receiving this because you were mentioned.Message ID: @.***>

[jbytecode]

@reidjohnson - Do you have suggestions for suitable reviewers? You can use the list of people to search suitable reviewers for this submission. If so, please mention their GitHub usernames here without using the @ character. Thank you in advance.

[jbytecode]

@ArkajyotiSaha, @bcjaeger - Thank you for the response. Hope to work together in future works.

[reidjohnson]

@jbytecode Here are some suggestions based on a cursory review of their familiarity with Python, ML, uncertainty estimation, and/or tree-based models:

astrogilda oparisot jncraton xiaowuc2 brunaw YehorYudinIPP shahmoradi

[jbytecode]

👋👋👋 Dear @astrogilda, @oparisot , @jncraton👋👋👋

Would you be willing to assist in reviewing this submission for JOSS (Journal of Open Source Software)?

JOSS publishes articles about open source research software. The submission I'd like you to review is titled:

quantile-forest: A Python Package for Quantile Regression Forests

You can find more information at the top of this Github issue (https://github.com/openjournals/joss-reviews/issues/5795).

The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. If you have any questions please let me know.

This is the pre-review issue. After setting at least 2 reviewers we will start the review process in a separate thread. In that thread, there will be 23 check items for each single reviewer.

Thank you in advance!

[salrm8]

Hi @jbytecode If the due date can be in December, I would be happy to review the package.

[jncraton]

@jbytecode I am also happy to review this.

[astrogilda]

I can review it as well, as long as the due date is in December, post NeurIPS. Prior commitments.

On Mon, Oct 23, 2023, 10:25 AM Jon Craton @.***> wrote:

@jbytecode https://github.com/jbytecode I am also happy to review this.

— Reply to this email directly, view it on GitHub https://github.com/openjournals/joss-reviews/issues/5795#issuecomment-1775324545, or unsubscribe https://github.com/notifications/unsubscribe-auth/AFTOOHXZP3USXE4EETFVE7DYAZ45LAVCNFSM6AAAAAA4IJ53XSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZVGMZDINJUGU . You are receiving this because you were mentioned.Message ID: @.***>

[jbytecode]

@editorialbot add @jncraton as reviewer

[editorialbot]

I'm sorry human, I don't understand that. You can see what commands I support by typing:

@editorialbot commands

[jbytecode]

@editorialbot add @jncraton as reviewer

[editorialbot]

@jncraton added to the reviewers list!

[jbytecode]

@editorialbot add @salrm8 as reviewer

[editorialbot]

@salrm8 added to the reviewers list!

[jbytecode]

@editorialbot add @astrogilda as reviewer

[editorialbot]

@astrogilda added to the reviewers list!

[jbytecode]

@editorialbot start review

@jncraton, @salrm8, @astrogilda - Thank you for accepting our invitation. The review will start in a separate thread. I'll introduce the instructions there.

[editorialbot]

OK, I've started the review over in https://github.com/openjournals/joss-reviews/issues/5976.

[astrogilda]

I can review it as well, as long as the due date is in December, post NeurIPS. Prior commitments.

On Mon, Oct 23, 2023, 7:56 AM Mehmet Hakan Satman @.***> wrote:

👋👋👋 Dear @astrogilda https://github.com/astrogilda, @oparisot https://github.com/oparisot , @jncraton https://github.com/jncraton 👋👋👋

Would you be willing to assist in reviewing this submission for JOSS (Journal of Open Source Software)?

JOSS publishes articles about open source research software. The submission I'd like you to review is titled:

quantile-forest: A Python Package for Quantile Regression Forests

You can find more information at the top of this Github issue (#5795 https://github.com/openjournals/joss-reviews/issues/5795).

The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. If you have any questions please let me know.

This is the pre-review issue. After setting at least 2 reviewers we will start the review process in a separate thread. In that thread, there will be 23 check items for each single reviewer.

Thank you in advance!

— Reply to this email directly, view it on GitHub https://github.com/openjournals/joss-reviews/issues/5795#issuecomment-1775030893, or unsubscribe https://github.com/notifications/unsubscribe-auth/AFTOOHRHTUBVPRSPRWTQLNDYAZLNLAVCNFSM6AAAAAA4IJ53XSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZVGAZTAOBZGM . You are receiving this because you were mentioned.Message ID: @.***>

[oparisot]

Hello,

Ok for me to review!

Olivier Parisot

Le lun. 23 oct. 2023, 14:27, Saleh Rezaeiravesh @.***> a écrit :

Hi @jbytecode https://github.com/jbytecode If the due date can be in December, I would be happy to review the package.

— Reply to this email directly, view it on GitHub https://github.com/openjournals/joss-reviews/issues/5795#issuecomment-1775081208, or unsubscribe https://github.com/notifications/unsubscribe-auth/AEHLM3VP7VQKDFR3UCR3UTTYAZPCXAVCNFSM6AAAAAA4IJ53XSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZVGA4DCMRQHA . You are receiving this because you were mentioned.Message ID: @.***>