editorialbot commented 1 year ago

Submitting author: !--author-handle-->@lbluque@jbytecode<!--end-editor-- Reviewers: @htjb, @mhu48 Archive: 10.5281/zenodo.10246640

Status

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Markdown: [![status](https://joss.theoj.org/papers/7ff5b2d9175c3e316ff7e9ac02d15540/status.svg)](https://joss.theoj.org/papers/7ff5b2d9175c3e316ff7e9ac02d15540)

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Please avoid lengthy details of difficulties in the review thread. Instead, please create a new issue in the target repository and link to those issues (especially acceptance-blockers) by leaving comments in the review thread below. (For completists: if the target issue tracker is also on GitHub, linking the review thread in the issue or vice versa will create corresponding breadcrumb trails in the link target.)

Reviewer instructions & questions

@htjb & @mhu48, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review. First of all you need to run this command in a separate comment to create the checklist:

@editorialbot generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @jbytecode know.

✨ 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 @htjb

📝 Checklist for @mhu48

editorialbot commented 1 year ago

Hello humans, I'm @editorialbot, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

@editorialbot commands

For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

@editorialbot generate pdf

editorialbot commented 1 year ago

Software report:

github.com/AlDanial/cloc v 1.88  T=0.13 s (467.3 files/s, 64596.1 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          32            942           2172           3802
Markdown                         5             64              0            260
YAML                             8             49             21            254
TeX                              1             19              0            239
reStructuredText                10             67             52            128
TOML                             1             11              3             69
INI                              1              0              0              2
JSON                             1              0              0              1
-------------------------------------------------------------------------------
SUM:                            59           1152           2248           4755
-------------------------------------------------------------------------------

gitinspector failed to run statistical information for the repository

editorialbot commented 1 year ago

Wordcount for paper.md is 1276

editorialbot commented 1 year ago

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

OK DOIs

- 10.1111/j.2517-6161.1996.tb02080.x is OK
- 10.1198/016214506000000735 is OK
- 10.1080/00401706.1967.10490502 is OK
- 10.1214/15-AOS1388 is OK
- 10.1287/opre.2015.1436 is OK
- 10.1111/j.1467-9868.2005.00532.x is OK
- 10.1080/10618600.2012.681250 is OK
- 10.1007/s00362-017-0882-z is OK
- 10.21105/joss.03024 is OK
- 10.1103/PhysRevB.106.144202 is OK
- 10.1103/PhysRevB.100.134108 is OK

MISSING DOIs

- None

INVALID DOIs

- None

editorialbot commented 1 year ago

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

jbytecode commented 1 year ago

@htjb, @mhu48

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!

htjb commented 1 year ago

Review checklist for @htjb

Conflict of interest

[x] 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

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/CederGroupHub/sparse-lm?
[x] License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
[x] Contribution and authorship: Has the submitting author (@lbluque) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[x] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[x] 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.
[x] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] 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

[x] Installation: Does installation proceed as outlined in the documentation?
[x] Functionality: Have the functional claims of the software been confirmed?
[x] 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

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

[x] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[x] 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?
[x] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[x] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[x] 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 commented 1 year ago

@mhu48 - Could you please generate your checklist before starting your review? Thank you in advance.

mhu48 commented 1 year ago

Review checklist for @mhu48

Conflict of interest

[x] 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

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/CederGroupHub/sparse-lm?
[x] License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
[x] Contribution and authorship: Has the submitting author (@lbluque) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[x] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[x] 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.
[x] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] 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

[x] Installation: Does installation proceed as outlined in the documentation?
[x] Functionality: Have the functional claims of the software been confirmed?
[x] 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

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

[x] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[x] 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?
[x] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[x] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[x] 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?

htjb commented 1 year ago

@lbluque this is a nice piece of code that I believe fills a gap in the capabilities of sklearn. The documentation is well written and formatted as is the paper.

I had a few comments on the code and paper below.

Paper

I think in line 56 there is an unrendered symbol between and and are.
Are you aware of any particular examples of sparse-lm being used in the literature? If so it might be good to reference these somewhere in the paper.
You mention a few different packages that can be used to fit some specific sparse linear regression models. My understanding is that sparse-lm aims to be a catch all for the things missing in scikit-learn so that the user only needs to install one package in addition to sklearn. In the paper where you list the implemented regression models and model selection tools I think it would be good to add which packages currently feature these models e.g. (celer and grupyr) next to Lasso and Group Lasso. This will help to emphasis the benefits of having all of the models in one place and identify any gaps in the existing toolbox.
I think it would be nice to see a physically motivated example/use case in the paper just to emphasis how important the tool is.
I think nore on line 45 should be nor.

Code

Could you please clarify for my benefit why you have reimplemented the GridSearchCV functionality in sklearn? You use the sklearn grid search in the readme example.
It appears that the example in the readme crashes. This should ideally be fixed.
There is a nice guide for contributing to the code in the documentation but it would be good to have this information in the README to since this is the first port of call for most people before looking at the documentation. I would just add a section saying contributions are welcome and link the documentation page to the readme.
Similarly I would add install instructions to the README.
I would add a list of requirements to the readme and maybe a requirements.txt. I'm aware that they are listed in the pyproject.toml but this is harder to parse than a requirements.txt.
The API documentation is very thorough but there are a few formatting issues that it would be good to check over. e.g for sparselm.model.RidgedBestSubsetSelection under VARIABLES: and a few of the other models. Similarly in GridSearchCV.

lbluque commented 1 year ago

Thanks a lot, @htjb for the thorough review and comments! These are quite helpful, I'll start addressing them as soon as I can.

jbytecode commented 1 year ago

@lbluque - Could you please set the license of the software explicitly as defined in https://joss.readthedocs.io/en/latest/submitting.html. The license should be a in OSI-approved license

lbluque commented 1 year ago

@lbluque - Could you please set the license of the software explicitly as defined in https://joss.readthedocs.io/en/latest/submitting.html. The license should be a in OSI-approved license

Hi @jbytecode. I am not sure what you mean by setting it explicitly. We have a BSD-3-clause-lbnl license, which is osi approved in our LICENSE file.

Can you please point out if I have missed anything?

jbytecode commented 1 year ago

@lbluque - Thank you, I mean the license type on the right side of the GitHub page, however, I have just realized the license link is linked to the license file that indicates it is licensed under BSD-3-clause.

lbluque commented 1 year ago

Ah, got it! Unfortunately, I think Github can't parse our specific license to have the link mention 3-BSD explicitly.

jbytecode commented 1 year ago

@lbluque - That makes sense, thank you for the clarification.

lbluque commented 1 year ago

I finally had some time to make edits. Thanks for your patience and your thorough review @htjb!

Paper
* I think in line 56 there is an unrendered symbol between `and` and `are`. 
Fixed, thanks.
* Are you aware of any particular examples of `sparse-lm` being used in the literature? If so it might be good to reference these somewhere in the paper.
I am only aware of our own work. I have added some citations at the end of the end of the Statement of Need section (though it feels a bit like a self-plug :electric_plug: )
* You mention a few different packages that can be used to fit some specific sparse linear regression models. My understanding is that `sparse-lm` aims to be a catch all for the things missing in `scikit-learn` so that the user only needs to install one package in addition to `sklearn`. In the paper where you list the implemented regression models and model selection tools I think it would be good to add which packages currently feature these models e.g. (`celer` and `grupyr`) next to Lasso and Group Lasso. This will help to emphasis the benefits of having all of the models in one place and identify any gaps in the existing toolbox.
Great suggestion! I added a table in the usage section that makes it much more clear what is and isn't available in the different packages.
* I think it would be nice to see a physically motivated example/use case in the paper just to emphasis how important the tool is.
I personally prefer to exclude direct examples in JOSS papers to keep the manuscript short and concise. And especially for a package like sparse-lm with applicability in a very wide range of fields, I think it can also over-emphasize a specific field. In lieu, I added citations to many applications in various fields of regression with structured sparsity in the Summary and Statement of need sections. Please let me know your thoughts.
* I think `nore` on line 45 should be `nor`.
Fixed thank you!

Code
* Could you please clarify for my benefit why you have reimplemented the `GridSearchCV` functionality in `sklearn`? You use the `sklearn` grid search in the readme example.
The only reason is that the GridSearchCV implemented in sparse-lm allows to automatically pick hyperparameters based on the "1-std rule" (1 std from the optimum, and those favor sparsity over CV error), rather thank the optimal value given by the sklearn version, otherwise they are exactly the same.
* It appears that the example in the readme crashes. This should ideally be fixed.
Fixed as well. Thanks a lot!
* There is a nice guide for contributing to the code in the documentation but it would be good to have this information in the README to since this is the first port of call for most people before looking at the documentation. I would just add a section saying contributions are welcome and link the documentation page to the readme.
Very good point. Added links to README to make it easier for users and devs.
* Similarly I would add install instructions to the README.
Same as above.
* I would add a list of requirements to the readme and maybe a `requirements.txt`. I'm aware that they are listed in the `pyproject.toml` but this is harder to parse than a `requirements.txt`.
Added a requirements.txt to the repo as well.
* The API documentation is very thorough but there are a few formatting issues that it would be good to check over. e.g for `sparselm.model.RidgedBestSubsetSelection` under `VARIABLES:` and a few of the other models. Similarly in `GridSearchCV`.
Thanks a lot for cathing these. I went in and think I have now fixed all these formatting issues.

Please have a look at these changes when you get a chance, and let us know if you have further comments or suggestions. Thanks again!

lbluque commented 1 year ago

@editorialbot generate pdf

editorialbot commented 1 year ago

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

lbluque commented 1 year ago

@editorialbot generate pdf

editorialbot commented 1 year ago

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

htjb commented 1 year ago

Hi @lbluque, the changes look good! The table is a nice addition to the paper and the readme is looking good.

I can understand your point about adding examples in to the paper. I think referencing different fields where sparse linear regression models are being used is a reasonable alternative and referencing a paper where the package has already been used in the statement of need is great.

The package is a nice addition to the python toolbox! @jbytecode I ticked off my checklist. Please let me know if there is anything else I need to do.

@lbluque neat work!

jbytecode commented 1 year ago

@htjb - Thank you for your review.

@mhu48 - Could you please update your status and inform us on how is your review going? Thank you in advance.

jbytecode commented 1 year ago

@mhu48 - Could you please update your status and inform us on how is your review going? Thank you in advance.

mhu48 commented 1 year ago

Thank you fo the reminder, apparently something interesting happened last week and I thought I already submitted the changes on github. Nevertheless, I have updated the list just now. Please let me know if it is still not reflected.

On a side note, how do I know how many papers I have reviewed for JOSS so far? I tried searching for email chains but that was quite a mess. Thanks so much for your help in advance.

Best,

Mign

On Sun, Nov 19, 2023 at 11:47 AM Mehmet Hakan Satman < @.***> wrote:

@mhu48 https://github.com/mhu48 - Could you please update your status and inform us on how is your review going? Thank you in advance.

— Reply to this email directly, view it on GitHub https://github.com/openjournals/joss-reviews/issues/5867#issuecomment-1817929029, or unsubscribe https://github.com/notifications/unsubscribe-auth/AKGGVY2LHAPHL2H5MSCMHYLYFJA45AVCNFSM6AAAAAA5CSXLOSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMJXHEZDSMBSHE . You are receiving this because you were mentioned.Message ID: @.***>

jbytecode commented 1 year ago

@mhu48 - Do you have any suggestions/corrections/additions due to your review?

I think you can see a list of reviewed manuscripts by you in this page

jbytecode commented 1 year ago

@mhu48 - Here is the list of the reviewed papers by you: Link

mhu48 commented 1 year ago

Awesome, thanks very much!

On Sun, Nov 19, 2023 at 11:57 AM Mehmet Hakan Satman < @.***> wrote:

@mhu48 https://github.com/mhu48 - Here is the list of the reviewed papers by you: Link https://joss.theoj.org/papers/reviewed_by/mhu48

— Reply to this email directly, view it on GitHub https://github.com/openjournals/joss-reviews/issues/5867#issuecomment-1817931399, or unsubscribe https://github.com/notifications/unsubscribe-auth/AKGGVYYOJLM3V66B45DYPY3YFJCCHAVCNFSM6AAAAAA5CSXLOSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMJXHEZTCMZZHE . You are receiving this because you were mentioned.Message ID: @.***>

jbytecode commented 1 year ago

@mhu48 - I can see you've already checked all of the tasks in your check list, however, it would be nice to have a review report of you here.

mhu48 commented 12 months ago

Hi,

Thank you for the reminder. I just saw your reply. Please find my report below.

"I'm pleased to report that the author has diligently addressed the concerns raised during the previous review process, and the changes made have significantly improved the overall quality of the package.

Specifically, the updated README file is now well-structured, providing clear and comprehensive documentation. The author has effectively incorporated the necessary information, making it accessible and user-friendly.

The concerns I previously raised and in the list of reviewer questions have been thoughtfully addressed, and the modifications implemented are not only acceptable but enhance the clarity and usability of the package. The inclusion of additional details has notably improved the overall robustness of the code.

In conclusion, this is a nice addition to the Python toolbox, offering valuable functionality for sparse linear regression models."

Best,

Ming

On Sun, Nov 19, 2023 at 12:02 PM Mehmet Hakan Satman < @.***> wrote:

@mhu48 https://github.com/mhu48 - I can see you've check all of the tasks in your check list, however, it would be nice to have a review report of you here.

— Reply to this email directly, view it on GitHub https://github.com/openjournals/joss-reviews/issues/5867#issuecomment-1817932656, or unsubscribe https://github.com/notifications/unsubscribe-auth/AKGGVY5GF7LJGTUT2L2EY6DYFJCUVAVCNFSM6AAAAAA5CSXLOSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMJXHEZTENRVGY . You are receiving this because you were mentioned.Message ID: @.***>

jbytecode commented 12 months ago

@editorialbot check references

editorialbot commented 12 months ago

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

OK DOIs

- 10.1111/j.2517-6161.1996.tb02080.x is OK
- 10.1198/016214506000000735 is OK
- 10.1080/00401706.1967.10490502 is OK
- 10.1214/15-AOS1388 is OK
- 10.1287/opre.2015.1436 is OK
- 10.1111/j.1467-9868.2005.00532.x is OK
- 10.1080/10618600.2012.681250 is OK
- 10.1007/s00362-017-0882-z is OK
- 10.21105/joss.03024 is OK
- 10.1257/jep.31.2.3 is OK
- 10.23919/CCC52363.2021.9549471 is OK
- 10.1080/03610918.2011.611311 is OK
- 10.1039/C7RE00210F is OK
- 10.1186/1471-2105-8-60 is OK
- 10.1103/PhysRevB.100.134108 is OK
- 10.1016/j.commatsci.2022.112000 is OK
- 10.1103/PhysRevB.106.024203 is OK
- 10.1103/PRXEnergy.2.043005 is OK
- 10.1103/PhysRevB.106.144202 is OK

MISSING DOIs

- None

INVALID DOIs

- None

jbytecode commented 12 months ago

@editorialbot generate pdf

editorialbot commented 12 months ago

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

jbytecode commented 12 months ago

Post-Review Checklist for Editor and Authors

Additional Author Tasks After Review is Complete

[ ] Double check authors and affiliations (including ORCIDs)
[ ] Make a release of the software with the latest changes from the review and post the version number here. This is the version that will be used in the JOSS paper.
[ ] Archive the release on Zenodo/figshare/etc and post the DOI here.
[ ] Make sure that the title and author list (including ORCIDs) in the archive match those in the JOSS paper.
[ ] Make sure that the license listed for the archive is the same as the software license.

Editor Tasks Prior to Acceptance

[x] Read the text of the paper and offer comments/corrections (as either a list or a PR)
[x] Check the references in the paper for corrections (e.g. capitalization)
[x] Check that the archive title, author list, version tag, and the license are correct
[x] Set archive DOI with @editorialbot set <DOI here> as archive
[x] Set version with @editorialbot set <version here> as version
[x] Double check rendering of paper with @editorialbot generate pdf
[x] Specifically check the references with @editorialbot check references and ask author(s) to update as needed
[ ] Recommend acceptance with @editorialbot recommend-accept

jbytecode commented 12 months ago

@lbluque - I've just sent a pull request that includes some minor changes in both the bibtex and the manuscript. Could you please review the changes and then merge if it is okay to you.

Note: In equation 1, after the squre root, number of pipes at the start does not match the ones at the end ($\sqrt{|\mathbf{g}}||\beta_{\mathbf{g}}||_2 $). Could you please review the equation 1 and correct the notation?

Please ping me when you've done with it. Thank you in advance.

$$ \begin{equation} \beta^* = \underset{\beta}{\text{argmin}}\;||\mathbf{X} \beta - \mathbf{y}||^22 + (1-\alpha)\lambda\sum{\mathbf{g}\in G}\sqrt{|\mathbf{g} }||\beta_{\mathbf{g}}||_2 + \alpha\lambda||\beta||_1 \end{equation} $$

jbytecode commented 11 months ago

@editorialbot check references

editorialbot commented 11 months ago

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

OK DOIs

- 10.1111/j.2517-6161.1996.tb02080.x is OK
- 10.1198/016214506000000735 is OK
- 10.1080/00401706.1967.10490502 is OK
- 10.1214/15-AOS1388 is OK
- 10.1287/opre.2015.1436 is OK
- 10.1111/j.1467-9868.2005.00532.x is OK
- 10.1080/10618600.2012.681250 is OK
- 10.1007/s00362-017-0882-z is OK
- 10.21105/joss.03024 is OK
- 10.1257/jep.31.2.3 is OK
- 10.23919/CCC52363.2021.9549471 is OK
- 10.1080/03610918.2011.611311 is OK
- 10.1039/C7RE00210F is OK
- 10.1186/1471-2105-8-60 is OK
- 10.1103/PhysRevB.100.134108 is OK
- 10.1016/j.commatsci.2022.112000 is OK
- 10.1103/PhysRevB.106.024203 is OK
- 10.1103/PRXEnergy.2.043005 is OK
- 10.1103/PhysRevB.106.144202 is OK

MISSING DOIs

- None

INVALID DOIs

- None

jbytecode commented 11 months ago

@editorialbot generate pdf

editorialbot commented 11 months ago

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

lbluque commented 11 months ago

@lbluque - I've just sent a pull request that includes some minor changes in both the bibtex and the manuscript. Could you please review the changes and then merge if it is okay to you.

Note: In equation 1, after the squre root, number of pipes at the start does not match the ones at the end (|g||βg||2). Could you please review the equation 1 and correct the notation?

Please ping me when you've done with it. Thank you in advance.

β∗=argminβ;||Xβ−y||22+(1−α)λ∑g∈G|g||βg||2+αλ||β||1

Thanks a for the detailed checks @jbytecode! I fixed the above and pushed a few edits to the paper. Please have a look when you get a chance.

lbluque commented 11 months ago

@editorialbot generate pdf

editorialbot commented 11 months ago

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

jbytecode commented 11 months ago

@lbluque - the paper looks better now, thank you.

Could you please perform these tasks:

Create a tagged release that includes all changes (the latest form) of the software. The version should be in form of vx.y.z, e.g., v1.0.3.
Create a Zenodo archive. In the archive, the title and the author list of the original paper should match. Do not use any other custom titles.
Report me the version tag, zenodo archive url, and the DOI given by Zenodo here.

Meanwhile you are fulfilling the requirements, I will be taking a look at the paper again.

Thank you in advance.

lbluque commented 11 months ago

Hi @jbytecode - here is the release and zenodo archive information:

release v0.5.2
10.5281/zenodo.10246640
Archive URL: https://zenodo.org/records/10246640

jbytecode commented 11 months ago

@editorialbot set v0.5.2 as version

editorialbot commented 11 months ago

Done! version is now v0.5.2

jbytecode commented 11 months ago

@lbluque - Thank you, I've just registered the version id. Could you please add ORCIDs of authors in the archive and then ping me again?

lbluque commented 11 months ago

Thanks @jbytecode - added orcids!

jbytecode commented 11 months ago

@editorialbot set 10.5281/zenodo.10246640 as archive

openjournals / joss-reviews

[REVIEW]: sparse-lm: Sparse linear regression models in Python #5867

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