[REVIEW]: giotto-deep: A Python Package for Topological Deep Learning

[editorialbot]

Submitting author: @matteocao (Matteo Caorsi)
Repository: https://github.com/giotto-ai/giotto-deep
Branch with paper.md (empty if default branch): paper
Version: v0.0.3
Editor: @osorensen
Reviewers: @EduPH, @leotrs, @ismailguzel
Archive: 10.5281/zenodo.7243721

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Checklists

📝 Checklist for @EduPH

📝 Checklist for @leotrs

📝 Checklist for @ismailguzel

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Software report:

github.com/AlDanial/cloc v 1.88  T=0.01 s (201.3 files/s, 13287.6 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Markdown                         1             15              0             77
TeX                              1              9              0             74
YAML                             1              1              4             18
-------------------------------------------------------------------------------
SUM:                             3             25              4            169
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gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 663

[editorialbot]

Failed to discover a valid open source license

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

[editorialbot]

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

OK DOIs

- None

MISSING DOIs

- 10.1017/9781316671665.004 may be a valid DOI for title: Topological data analysis
- 10.1145/1873951.1874254 may be a valid DOI for title: Torchvision the machine-vision package of torch
- 10.3389/frai.2021.681108 may be a valid DOI for title: A survey of topological machine learning methods

INVALID DOIs

- None

[leotrs]

Review checklist for @leotrs

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/giotto-ai/giotto-deep?
• 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 (@matteocao) 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?

[EduPH]

Review checklist for @EduPH

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/giotto-ai/giotto-deep?
• 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 (@matteocao) 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?

[leotrs]

Hello @matteocao! The paper has three authors and I see that all three are contributors to the repository. However, there are two contributors that i) are not authors, and ii) have contributed more than one contributor who is an author (at least by GitHub's measures here)

Could you please clarify how authorship was determined for your JOSS submission?

[matteocao]

Dear @leotrs ,
Thank you for your question.
Of the two contributors (that are not authors) you mention, one has joined the efforts very recently and hence has not (yet) contributed to any design/core functionality of the library. The other one, on the other hand, after the initial contributions (1y ago) has completely gone silent and still today we are not able to reach him. This is why we did not include him.
Let me know if this answer is satisfactory enough and/or if you have any suggestion on how to best proceed.

To further clarify: @giotto-learn and @matteocao are the same physical person.

[leotrs]

Thanks for the clarification. I think the current author list makes sense. Do consider to add the names of other/past contributors in the acknowledgements section of the paper.

[ismailguzel]

Review checklist for @ismailguzel

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/giotto-ai/giotto-deep?
• 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 (@matteocao) 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?

[leotrs]

@matteocao I can see your CI runs pytest on the root directory but wasn't able to find a directory containing a test suite. Could you please point me to it? Thanks!

[leotrs]

@matteocao I see no mention of related/similar software in the current paper. Are there absolutely no other options for using topological techniques in deep learning?

[matteocao]

Dear @leotrs ,
Thank you for your message.

@matteocao I can see your CI runs pytest on the root directory but wasn't able to find a directory containing a test suite. Could you please point me to it? Thanks!

The unit tests are written per module, meaning that there is a folder called tests in each module folder. Here some examples:

• https://github.com/giotto-ai/giotto-deep/tree/master/gdeep/data/datasets/tests
• https://github.com/giotto-ai/giotto-deep/tree/master/gdeep/data/persistence_diagrams/tests
• https://github.com/giotto-ai/giotto-deep/tree/master/gdeep/data/preprocessors/tests
• https://github.com/giotto-ai/giotto-deep/tree/master/gdeep/models/tests
• ...

The integration tests (or maybe even callable e2e tests) are the notebookes themselves: all of those in here

We find this way of organising tests reasonable (as opposed to putting all tests in one single folder). Please let us know if this feels reasonable for you as well.

@matteocao I see no mention of related/similar software in the current paper. Are there absolutely no other options for using topological techniques in deep learning?

There are indeed no package that try to do what we do. I would like however to mention the only possibly similar one, though much more limited in scope: https://github.com/MathieuCarriere/perslay. This packages implements one single technique that is topology-flavoured for feed-forward deep networks. Important note: even though we understood the paper (and we did cite such paper) and later were able to reproduce its content, we have not been able to use ay part of this "PersLay" package due to the total lack of documentation and structure. Please feel free to advise on what's the best course of action. Thank you!

[leotrs]

Both responses sound reasonable to me. Thanks.

[leotrs]

I have finished my review and I believe the paper can be accepted as is.

[osorensen]

Thanks a lot for your time and efforts, @leotrs

[EduPH]

Congratulations @matteocao, it is a nice library for the TDA community. I think the paper can be accepted. Just a remark, it would be nice to mention in the paper that previously, people have used TDA libraries such as Gudhi together with deep learning libraries such as scikit-learn, TensorFlow or PyTorch.

[ismailguzel]

I tested the package installation on both Windows and Ubuntu. Good for both platforms, actually. I'm grateful for @matteocao
. I also didn't see anything different from what other reviewers had already noted. Finally, my review is completed, and I think the document can be accepted just as it is. It will be a great package for TDA folks.

[osorensen]

Wow! Thanks to all of @EduPH, @leotrs, @ismailguzel for completing your reviews so quickly!

@matteocao, I will now read through the paper a final time, and let you know if I have any suggested changes. In the meantime, could you please

• Make a tagged release of your software, and list the version tag of the archived version here.
• Archive the reviewed software in Zenodo or a similar service (e.g., figshare, an institutional repository)
• Check the archival deposit (e.g., in Zenodo) has the correct metadata. This includes the title (should match the paper title) and author list (make sure the list is correct and people who only made a small fix are not on it). You may also add the authors' ORCID.
• Please list the DOI of the archived version here.

[osorensen]

@editorialbot generate pdf

[osorensen]

@editorialbot check references

[osorensen]

@editorialbot set v0.0.3 as version

[editorialbot]

Done! version is now v0.0.3

[osorensen]

@editorialbot set v0.0.3 as version

[editorialbot]

Done! version is now v0.0.3

[osorensen]

@editorialbot set 10.5281/zenodo.7243721 as archive

[editorialbot]

Done! Archive is now 10.5281/zenodo.7243721

[osorensen]

Sorry for all the posts, I mixed up the commands a bit :-)

[osorensen]

@editorialbot recommend-accept

[editorialbot]

Attempting dry run of processing paper acceptance...

[editorialbot]

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

Check final proof 👉📄 Download article

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[arfon]

@editorialbot accept

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

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[arfon]

@EduPH, @leotrs, @ismailguzel – many thanks for your reviews here and to @osorensen 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 ✨

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

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[matteocao]

Thank you All!

I really love this approach to peer reviews: fully transparent, useful, serious and friendly at the same time. I think JOSS is really bringing a breath of fresh air into the world of publications and imho many publishers should follow this virtuous example!

Congrats for the great journal!

Cheers! 🥂