[REVIEW]: pudu: A Python library for agnostic feature selection and explainability of Machine Learning classification and regression problems.

[editorialbot]

Submitting author: @enricgrau (Enric Grau-Luque)
Repository: https:/pudu-py/pudu
Branch with paper.md (empty if default branch): main
Version: 0.3.0
Editor: @arfon
Reviewers: @hbaniecki, @aksholokhov
Archive: 10.5281/zenodo.10161346

Status

Status badge code:

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Markdown: [![status](https://joss.theoj.org/papers/cacb5b6520209b0c940bf46638df251d/status.svg)](https://joss.theoj.org/papers/cacb5b6520209b0c940bf46638df251d)

Reviewers and authors:

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

@hbaniecki & @aksholokhov, 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 @arfon 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 @hbaniecki

📝 Checklist for @aksholokhov

[editorialbot]

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:

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

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.88  T=0.11 s (775.3 files/s, 281992.0 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
JavaScript                      15           2433           2497           9214
HTML                            19           1540             54           7508
SVG                              1              0              0           2671
Python                          19            600            783           1206
CSS                              4            185             35            762
XML                              1              0              2            711
TeX                              1             18              0            350
reStructuredText                12            161            106            293
YAML                             9             31             47            228
Markdown                         5             58              0            145
TOML                             1              0              1              3
-------------------------------------------------------------------------------
SUM:                            87           5026           3525          23091
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 1061

[editorialbot]

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

OK DOIs

- 10.1016/B978-1-55860-247-2.50037-1 is OK
- 10.1007/3-540-57868-4_57 is OK
- 10.1016/J.CJCA.2021.09.004 is OK
- 10.48550/arxiv.1602.04938 is OK
- 10.48550/arxiv.2010.07389 is OK
- 10.1613/JAIR.1.12228 is OK
- 10.1109/ACCESS.2020.2976199 is OK
- 10.1145/3351095.3375624 is OK
- 10.3389/FDATA.2021.688969 is OK
- 10.1109/iccv.2017.74 is OK
- 10.5281/ZENODO.6344451 is OK
- 10.1109/MCSE.2007.55 is OK
- 10.1038/s41586-020-2649-2 is OK

MISSING DOIs

- 10.1109/cvpr.2017.354 may be a valid DOI for title: Network Dissection: Quantifying Interpretability of Deep Visual Representation

INVALID DOIs

- None

[arfon]

@hbaniecki, @aksholokhov – This is the review thread for the paper. All of our communications will happen here from now on.

Please read the "Reviewer instructions & questions" in the first comment above. Please create your checklist typing:

@editorialbot generate my checklist

As you go over the submission, please check any items that you feel have been satisfied. There are also links to the JOSS reviewer guidelines.

The JOSS review is different from most other journals. Our goal is to work with the authors to help them meet our criteria instead of merely passing judgment on the submission. As such, the reviewers are encouraged to submit issues and pull requests on the software repository. When doing so, please mention https:/openjournals/joss-reviews/issues/5873 so that a link is created to this thread (and I can keep an eye on what is happening). Please also feel free to comment and ask questions on this thread. In my experience, it is better to post comments/questions/suggestions as you come across them instead of waiting until you've reviewed the entire package.

We aim for the review process to be completed within about 4-6 weeks but please make a start well ahead of this as JOSS reviews are by their nature iterative and any early feedback you may be able to provide to the author will be very helpful in meeting this schedule.

[editorialbot]

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

[hbaniecki]

Review checklist for @hbaniecki

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:/pudu-py/pudu? yes
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license? MIT
• Contribution and authorship: Has the submitting author (@enricgrau) made major contributions to the software? Does the full list of paper authors seem appropriate and complete? Authors contribution with CRediT
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines In its current state no yes
• 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. No data
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item. No results
• 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. No data

Functionality

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

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is? In its current state no yes
• 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). https://pudu-py.github.io/pudu/examples.html
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)? https://pudu-py.github.io/pudu/index.html
• 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 https://pudu-py.github.io/pudu/contributions.html

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? High-level yes
• 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? Unclear/incomplete yes
• State of the field: Do the authors describe how this software compares to other commonly-used packages? No partially
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)? Some errors fixed
• 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? No yes

[hbaniecki]

Apart from minor issues with documentation and examples, I have the following major concerns about this contribution:

1. Substantial scholarly effort is (currently) not evident. It is a relatively young software (4k downloads), there is no user base (no issues, no GitHub stars, no blogs or promotion of the software?), and no clear applications (e.g. no citations). With similar software existing already, {pudu} is not likely to be cited unless the software/paper clearly highlights its original purpose, e.g. an original subset of methods implemented, accessible visualisation, better API, unique audience.
2. Statement of need is unclear. I have a hard time understanding the motivation for developing this software. In its current state, the paper's title and description seem too generic. Should "sensitivity analysis" be highlighted in the title?
   • A clear use-case for this software would help, e.g. the paper mentions the RELIEF method but does not elaborate on applications/usefulness of the algorithm to show that the {pudu} package is indeed needed in situation X or for user Y.
   • The terminology of "Importance/speed/synergy/reactivation" is unclear and could be better described in the paper.
   • Adding a figure to the paper could help illustrate the statement of need.
3. State of the field is missing. There is no description of how this software compares to other similar packages. Consider the attached non-exhaustive list of software for explainability and feature selection. Relating {pudu} to other software could partially alleviate concerns mentioned in 1. & 2., i.e. show the added value of {pudu} over the already available solutions, motivate the need for such new package, highlight the target audience etc.

I am open to discussion and hope the software paper can be improved to clearly state the motivation and effort.

References (non-exhaustive list)

• Molnar et al. iml: An R package for Interpretable Machine Learning. JOSS 2018
• Alber et al. iNNvestigate Neural Networks! JMLR 2019
• Kokhlikyan et al. Captum: A unified and generic model interpretability library for PyTorch. arXiv:2009.07896 2020
• Arya et al. AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. JMLR 2020
• (ours) dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python. JMLR 2021
• Klaise et al. Alibi Explain: Algorithms for Explaining Machine Learning Models. JMLR 2021
• Li et al. InterpretDL: Explaining Deep Models in PaddlePaddle. JMLR 2022
• Zhu et al. abess: A Fast Best-Subset Selection Library in Python and R. JMLR 2022

[hbaniecki]

Quality of writing

• There is some vague language, e.g. "help make sense of machine learning results", "Easy plotting of the results"
• Acronyms "ML/XAI" are defined but later unused
• Typos in references: "Bhatt et al. (2020)](Belle & Papantonis, 2021)" "Bau2018"

[aksholokhov]

Review checklist for @aksholokhov

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:/pudu-py/pudu?
• 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 (@enricgrau) 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?

[aksholokhov]

@arfon my full review is here.

[arfon]

Thanks for your reviews @hbaniecki and @aksholokhov. @enricgrau – please take a look at the feedback from both reviewers and share your responses here. Of particular focus should be a response to @hbaniecki's feedback here: #5873 (comment)

[enricgrau]

Thanks to @hbaniecki and @aksholokhov for the impeccable reviews. We've been working on all of your comments and concerns during all these days, and we hope to fulfil and respond to all the raised points sometime in the next couple of weeks. Thank you @arfon for your attention to this review. We are excited to show how much the article and documentation have improved once we finish with the corrections.

[enricgrau]

@editorialbot generate pdf

[editorialbot]

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

[enricgrau]

@arfon We have modified the paper.md and asked the editorialbot to re-generate the pdf but it is rendering the same old version. Can you help us with this? Shall we wait more time to re-generate? Thank you!

Edit: Could this be due to version change from v0.3.0 to v0.3.2?

[enricgrau]

@editorialbot generate pdf

[editorialbot]

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

[enricgrau]

@editorialbot set v0.3.2 as version

[editorialbot]

I'm sorry @enricgrau, I'm afraid I can't do that. That's something only editors are allowed to do.

[enricgrau]

@editorialbot generate pdf

[editorialbot]

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

[enricgrau]

@arfon I have made a final revision and created the Zenodo archive with the final version. I changed the title to match the paper and added all the authors. The DOI is 10.5281/zenodo.10161346

[enricgrau]

@arfon Just friendly reminder. Thank you!

[arfon]

@enricgrau – my apologies, somehow I lost track of this one!

[arfon]

@editorialbot set 10.5281/zenodo.10161346 as archive

[editorialbot]

Done! archive is now 10.5281/zenodo.10161346

[arfon]

@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

- 10.1016/S0924-2031(03)00045-6 is OK
- 10.3390/analytica3030020 is OK
- 10.1038/srep19414 is OK
- 10.1201/9781003328513-9 is OK
- 10.1016/J.ECOENV.2022.114405 is OK
- 10.1002/9781119763406.CH8 is OK
- 10.1038/s41524-022-00884-7 is OK
- 10.1016/B978-1-55860-247-2.50037-1 is OK
- 10.1007/3-540-57868-4_57 is OK
- 10.1016/J.CJCA.2021.09.004 is OK
- 10.48550/arxiv.1602.04938 is OK
- 10.48550/arxiv.2010.07389 is OK
- 10.1613/JAIR.1.12228 is OK
- 10.1109/ACCESS.2020.2976199 is OK
- 10.1145/3351095.3375624 is OK
- 10.3389/FDATA.2021.688969 is OK
- 10.1109/iccv.2017.74 is OK
- 10.5281/ZENODO.6344451 is OK
- 10.1109/cvpr.2017.354 is OK
- 10.1109/MCSE.2007.55 is OK
- 10.1038/s41586-020-2649-2 is OK
- 10.1145/3313831.3376219 is OK
- 10.21105/JOSS.05220 is OK
- 10.1145/3351095.3375624 is OK
- 10.3389/FDATA.2021.688969 is OK
- 10.1002/AENM.202103163 is OK
- 10.1039/d1ta01299a is OK
- 10.5281/ZENODO.4743323 is OK

MISSING DOIs

- 10.21203/rs.3.rs-2963888/v1 may be a valid DOI for title: The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective

INVALID DOIs

- 10.1116/1.5140587/247679 is INVALID
- 10.1103/REVMODPHYS.79.353/FIGURES/62/MEDIUM is INVALID

[editorialbot]

⚠️ Error preparing paper acceptance. The generated XML metadata file is invalid.

ID ref-Bhatt2020 already defined
ID ref-Belle2021 already defined

[arfon]

@enricgrau – could you check your references in your BibTeX file please? It looks like there are duplicate entries for Bhatt2020 and Belle2021

[enricgrau]

@editorialbot generate pdf

[editorialbot]

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

[enricgrau]

@arfon No problem :) I fixed the doi's and also deleted de duplicate entries. Thank you!

[arfon]

@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

- 10.1016/S0924-2031(03)00045-6 is OK
- 10.3390/analytica3030020 is OK
- 10.1116/1.5140587 is OK
- 10.1038/srep19414 is OK
- 10.1201/9781003328513-9 is OK
- 10.1016/J.ECOENV.2022.114405 is OK
- 10.1002/9781119763406.CH8 is OK
- 10.1103/REVMODPHYS.79.353 is OK
- 10.1038/s41524-022-00884-7 is OK
- 10.1016/B978-1-55860-247-2.50037-1 is OK
- 10.1007/3-540-57868-4_57 is OK
- 10.1016/J.CJCA.2021.09.004 is OK
- 10.48550/arxiv.1602.04938 is OK
- 10.48550/arxiv.2010.07389 is OK
- 10.1613/JAIR.1.12228 is OK
- 10.1109/ACCESS.2020.2976199 is OK
- 10.3389/FDATA.2021.688969 is OK
- 10.1109/iccv.2017.74 is OK
- 10.5281/ZENODO.6344451 is OK
- 10.1109/cvpr.2017.354 is OK
- 10.1109/MCSE.2007.55 is OK
- 10.1038/s41586-020-2649-2 is OK
- 10.1145/3313831.3376219 is OK
- 10.21105/JOSS.05220 is OK
- 10.1145/3351095.3375624 is OK
- 10.1002/AENM.202103163 is OK
- 10.1039/d1ta01299a is OK
- 10.5281/ZENODO.4743323 is OK

MISSING DOIs

- 10.21203/rs.3.rs-2963888/v1 may be a valid DOI for title: The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective

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#4826, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

[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.

If using GitHub, a Cite this repository menu will appear in the About section, containing both APA and BibTeX formats. When exported to Zotero using a browser plugin, Zotero will automatically create an entry using the information contained in the .cff file.

You can copy the contents for your CITATION.cff file here:

CITATION.cff

cff-version: "1.2.0"
authors:
- family-names: Grau-Luque
  given-names: Enric
  orcid: "https://orcid.org/0000-0002-8357-5824"
- family-names: Becerril-Romero
  given-names: Ignacio
  orcid: "https://orcid.org/0000-0002-7087-6097"
- family-names: Perez-Rodriguez
  given-names: Alejandro
  orcid: "https://orcid.org/0000-0002-3634-1355"
- family-names: Guc
  given-names: Maxim
  orcid: "https://orcid.org/0000-0002-2072-9566"
- family-names: Izquierdo-Roca
  given-names: Victor
  orcid: "https://orcid.org/0000-0002-5502-3133"
doi: 10.5281/zenodo.10161346
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Grau-Luque
    given-names: Enric
    orcid: "https://orcid.org/0000-0002-8357-5824"
  - family-names: Becerril-Romero
    given-names: Ignacio
    orcid: "https://orcid.org/0000-0002-7087-6097"
  - family-names: Perez-Rodriguez
    given-names: Alejandro
    orcid: "https://orcid.org/0000-0002-3634-1355"
  - family-names: Guc
    given-names: Maxim
    orcid: "https://orcid.org/0000-0002-2072-9566"
  - family-names: Izquierdo-Roca
    given-names: Victor
    orcid: "https://orcid.org/0000-0002-5502-3133"
  date-published: 2023-12-12
  doi: 10.21105/joss.05873
  issn: 2475-9066
  issue: 92
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 5873
  title: "pudu: A Python library for agnostic feature selection and
    explainability of Machine Learning spectroscopic problems"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.05873"
  volume: 8
title: "pudu: A Python library for agnostic feature selection and
  explainability of Machine Learning spectroscopic problems"

If the repository is not hosted on GitHub, a .cff file can still be uploaded to set your preferred citation. Users will be able to manually copy and paste the citation.

Find more information on .cff files here and here.

[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.05873 joss-papers#4831
1. Wait five minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.05873
2. If everything looks good, then close this review issue.
3. Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

[arfon]

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

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

[editorialbot]

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

If you would like to include a link to your paper from your README use the following code snippets:

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[![DOI](https://joss.theoj.org/papers/10.21105/joss.05873/status.svg)](https://doi.org/10.21105/joss.05873)

HTML:
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  <img src="https://joss.theoj.org/papers/10.21105/joss.05873/status.svg" alt="DOI badge" >
</a>

reStructuredText:
.. image:: https://joss.theoj.org/papers/10.21105/joss.05873/status.svg
   :target: https://doi.org/10.21105/joss.05873

This is how it will look in your documentation:

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