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[REVIEW]: ClustForOpt: Time-series aggregation for optimization in Julia #1573

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whedon opened this issue Jul 16, 2019 · 19 comments

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@whedon
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commented Jul 16, 2019 •
edited by ahwillia

Submitting author: @holgerteichgraeber (Holger Teichgraeber)
Repository: https://github.com/holgerteichgraeber/ClustForOpt.jl
Version: v0.4.2
Editor: @danielskatz
Reviewer: @jgoldfar, @ahwillia
Archive: Pending

Status

status

Status badge code:

HTML: <a href="http://joss.theoj.org/papers/e3975d642975a19f5e2d7e43e3752066"><img src="http://joss.theoj.org/papers/e3975d642975a19f5e2d7e43e3752066/status.svg"></a>
Markdown: [![status](http://joss.theoj.org/papers/e3975d642975a19f5e2d7e43e3752066/status.svg)](http://joss.theoj.org/papers/e3975d642975a19f5e2d7e43e3752066)

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

@jgoldfar & @ahwillia, please carry out your review in this issue by updating the checklist below. If you cannot edit the checklist please:

  1. Make sure you're logged in to your GitHub account
  2. Be sure to accept the invite at this URL: https://github.com/openjournals/joss-reviews/invitations

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

✨ Please try and complete your review in the next two weeks ✨

Review checklist for @jgoldfar

Conflict of interest

Code of Conduct

General checks

  • Repository: Is the source code for this software available at the repository url?
  • License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
  • Version: Does the release version given match the GitHub release (v0.4.2)?
  • Authorship: Has the submitting author (@holgerteichgraeber) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?

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 function 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

  • Authors: Does the paper.md file include a list of authors with their affiliations?
  • A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
  • References: Do all archival references that should have a DOI list one (e.g., papers, datasets, software)?

Review checklist for @ahwillia

Conflict of interest

Code of Conduct

General checks

  • Repository: Is the source code for this software available at the repository url?
  • License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
  • Version: Does the release version given match the GitHub release (v0.4.2)?
  • Authorship: Has the submitting author (@holgerteichgraeber) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?

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 function 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

  • Authors: Does the paper.md file include a list of authors with their affiliations?
  • A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
  • References: Do all archival references that should have a DOI list one (e.g., papers, datasets, software)?

@whedon whedon assigned danielskatz Jul 16, 2019

@whedon whedon added the review label Jul 16, 2019

@whedon whedon referenced this issue Jul 16, 2019

Closed

[PRE REVIEW]: ClustForOpt: Time-series aggregation for optimization in Julia #1562

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commented Jul 16, 2019

Hello human, I'm @whedon, a robot that can help you with some common editorial tasks. @jgoldfar, @ahwillia it looks like you're currently assigned to review this paper 🎉.

⭐️ Important ⭐️

If you haven't already, you should seriously consider unsubscribing from GitHub notifications for this (https://github.com/openjournals/joss-reviews) repository. As a reviewer, you're probably currently watching this repository which means for GitHub's default behaviour you will receive notifications (emails) for all reviews 😿

To fix this do the following two things:

  1. Set yourself as 'Not watching' https://github.com/openjournals/joss-reviews:

watching

  1. You may also like to change your default settings for this watching repositories in your GitHub profile here: https://github.com/settings/notifications

notifications

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

@whedon commands

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

@whedon generate pdf
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commented Jul 16, 2019 

Attempting PDF compilation. Reticulating splines etc...
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commented Jul 16, 2019

👉 Check article proof 📄 👈

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commented Jul 16, 2019

Note: @ahwillia is traveling for the next few weeks. It will be about 3 weeks until he can work on this review.
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commented Jul 16, 2019

👋 @jgoldfar, @ahwillia - We'll do the review here - please read the comments above, and get started when you can. If you have any questions, please ask.

@whedon whedon assigned jgoldfar and ahwillia Jul 17, 2019

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commented Jul 23, 2019

👋 @jgoldfar, @ahwillia - We'll do the review here - please read the comments above, and get started when you can. If you have any questions, please ask.

👋Thank you all for offering to review, I look forward to your comments.
Tagging along co-authors @YoungFaithful and @arbrandt for reference.
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commented Jul 26, 2019

👋 @jgoldfar - have you had a chance to get started?
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commented Aug 1, 2019

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commented Aug 2, 2019

@whedon remind @ahwillia in 7 days
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commented Aug 2, 2019

Reminder set for @ahwillia in 7 days
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commented Aug 9, 2019

Yes; I will post my review within a few days

@jgoldfar - any update on this?
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commented Aug 9, 2019

Looking through this now. I'm generally very impressed and think we should be able to approve this in short order. I am confirming that I can install and execute the package now.

My biggest piece of feedback is that the README and description of the package should emphasize even more applications. I think this package will be broadly useful to many fields! For example, the first sentence of the README might lead users to think the package is for a very specialized purpose I would recommend editing to something like...

Current: "ClustForOpt is a julia implementation of unsupervised machine learning methods for finding representative periods for energy systems optimization problems."

Revised: "ClustForOpt is a julia implementation of unsupervised machine learning methods for detecting motifs, clustering, and quantifying similarity between time series datasets."

Likewise, in the subsequent paragraphs, I recommend adding some more example applications with citations. Segmentation and clustering of audio datasets should be an easy one to find.

It is of course okay to say something like "this package was originally developed for energy systems optimization" but I think emphasizing the generality of the package and the methods as much as possible will increase the impact of this work.

Full disclosure, I've worked on using very simple time warping methods for neural data (https://www.biorxiv.org/content/10.1101/661165v1), though those data show very different statistics and call for different modeling approaches. But I'm quite enthusiastic about this area of research.
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commented Aug 9, 2019

One final thought, I don't insist on changing the name of the repo, but something like "TimeSeriesClustering.jl" would seem to better capture the function of the package. The name ClustForOpt doesn't make it super clear what the package does...

Also can the authors comment on the differences between this package and other time series packages in julia (e.g TimeSeries.jl) in the paper / README? It would be nice to give users more guidance on the broader tools available in Julia for these kinds of modeling problems.
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commented Aug 9, 2019

👋 @ahwillia, please update us on how your review is going.
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commented Aug 10, 2019

Thank you for your feedback, these are great ideas! I am out for the weekend, and will get back to this next week.
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commented Aug 10, 2019

In case that there are any papers that you can recommend to read in the suggested application areas, suggestions are greatly appreciated.
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commented Aug 15, 2019

👋 @jgoldfar, @ahwillia - can you please use your checklists above to indicate what you think is ok, and what needs to be done, in addition to the comments @ahwillia has posted in this thread, and what I expect @jgoldfar to post soon.
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commented Aug 16, 2019

Checked my boxes...

@holgerteichgraeber
In case that there are any papers that you can recommend to read in the suggested application areas, suggestions are greatly appreciated.

Eamonn Keogh has a variety of methods and application papers to check out (e.g. https://www.cs.ucr.edu/~eamonn/MatrixProfile.html). I'm sure many other research groups have relevant papers as well. Please don't worry about being comprehensive, but the more references you can find the better.
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commented Aug 17, 2019

👋 @jgoldfar

Yes; I will post my review within a few days

Can you please go ahead and do this? (and check the boxes for items that are complete)
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