Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[PRE REVIEW]: cblearn: Comparison-based Machine Learning in Python #5896

Closed
editorialbot opened this issue Sep 28, 2023 · 26 comments
Closed
Assignees
Labels
pre-review Python TeX Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning waitlisted Submissions in the JOSS backlog due to reduced service mode.

Comments

@editorialbot
Copy link
Collaborator

editorialbot commented Sep 28, 2023

Submitting author: @dekuenstle (David-Elias Künstle)
Repository: https://github.com/cblearn/cblearn
Branch with paper.md (empty if default branch): joss
Version: v0.1.2
Editor: @mbarzegary
Reviewers: @haniyeka, @sherbold, @stsievert
Managing EiC: Arfon Smith

Status

status

Status badge code:

HTML: <a href="https://joss.theoj.org/papers/9b9ce7d05cd840818e6626229a17c39f"><img src="https://joss.theoj.org/papers/9b9ce7d05cd840818e6626229a17c39f/status.svg"></a>
Markdown: [![status](https://joss.theoj.org/papers/9b9ce7d05cd840818e6626229a17c39f/status.svg)](https://joss.theoj.org/papers/9b9ce7d05cd840818e6626229a17c39f)

Author instructions

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

@dekuenstle 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.

Editor instructions

The JOSS submission bot @editorialbot is here to help you find and assign reviewers and start the main review. To find out what @editorialbot can do for you type:

@editorialbot commands
@editorialbot editorialbot added pre-review Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning labels Sep 28, 2023
@editorialbot
Copy link
Collaborator Author

Hello human, 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
Copy link
Collaborator Author

Software report:

github.com/AlDanial/cloc v 1.88  T=0.12 s (740.0 files/s, 82464.4 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          59           1413           2777           3771
TeX                              1             32              2            372
reStructuredText                14            213            166            351
Markdown                         4             77              0            246
YAML                             5             14             25            163
DOS Batch                        1              8              1             26
TOML                             1              1              0             11
make                             1              4              7              9
INI                              1              0              0              6
-------------------------------------------------------------------------------
SUM:                            87           1762           2978           4955
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

@editorialbot
Copy link
Collaborator Author

Wordcount for paper.md is 1277

@editorialbot
Copy link
Collaborator Author

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

OK DOIs

- 10.48550/arXiv.1912.01666 is OK
- 10.1167/jov.22.14.3985 is OK
- 10.1167/jov.22.13.5 is OK
- 10.1167/jov.22.14.3232 is OK
- 10.1109/MLSP.2012.6349720 is OK
- 10.1167/3.8.5 is OK
- 10.1145/1559755.1559760 is OK
- 10.1167/17.1.37 is OK
- 10.1167/jov.20.4.19 is OK
- 10.1167/jov.20.9.14 is OK
- 10.1167/12.3.19 is OK
- 10.1038/s41562-020-00951-3 is OK
- 10.3758/s13428-019-01285-3 is OK
- 10.1109/TVCG.2014.2346978 is OK
- 10.1145/3380741 is OK
- 10.48550/arXiv.1309.0238 is OK
- 10.1038/s41586-020-2649-2 is OK
- 10.21105/joss.04517 is OK
- 10.48550/arXiv.1511.02254 is OK
- 10.1167/jov.23.9.5388 is OK
- 10.48550/arXiv.2211.16459 is OK

MISSING DOIs

- 10.1109/cvpr46437.2021.00355 may be a valid DOI for title: Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings
- 10.1609/hcomp.v1i1.13079 may be a valid DOI for title: The crowd-median algorithm
- 10.1109/icassp.2018.8461868 may be a valid DOI for title: The landscape of non-convex quadratic feasibility

INVALID DOIs

- None

@editorialbot
Copy link
Collaborator Author

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

@editorialbot
Copy link
Collaborator Author

Five most similar historical JOSS papers:

Autorank: A Python package for automated ranking of classifiers
Submitting author: @sherbold
Handling editor: @arfon (Active)
Reviewers: @JonathanReardon, @ejhigson
Similarity score: 0.8089

Multiple Inference: A Python package for comparing multiple parameters
Submitting author: @dsbowen
Handling editor: @vissarion (Active)
Reviewers: @blakeaw, @mattpitkin, @nhejazi
Similarity score: 0.8077

Efficiently Learning Relative Similarity Embeddings with Crowdsourcing
Submitting author: @stsievert
Handling editor: @ajstewartlang (Active)
Reviewers: @hoechenberger, @stain, @jorgedch
Similarity score: 0.8007

bwsample: Processing Best-Worst Scaling data
Submitting author: @ulf1
Handling editor: @mikldk (Retired)
Reviewers: @ejhigson, @jakryd
Similarity score: 0.7968

fieldcompare: A Python package for regression testing simulation results
Submitting author: @dglaeser
Handling editor: @danielskatz (Active)
Reviewers: @idoby, @WilliamJamieson
Similarity score: 0.7956

⚠️ 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.

@arfon arfon added the waitlisted Submissions in the JOSS backlog due to reduced service mode. label Sep 28, 2023
@arfon
Copy link
Member

arfon commented Sep 28, 2023

@dekuenstle - thanks for your submission to JOSS. We're currently managing a large backlog of submissions and the editor most appropriate for your area is already rather busy.

For now, we will need to waitlist this paper and process it as the queue reduces. Thanks for your patience!

@arfon
Copy link
Member

arfon commented Nov 24, 2023

@editorialbot invite @mbarzegary as editor

👋 @mbarzegary – would you be willing to edit this submission for JOSS?

@editorialbot
Copy link
Collaborator Author

Invitation to edit this submission sent!

@mbarzegary
Copy link

Hi @arfon
Yes, I can handle this.

@mbarzegary
Copy link

@editorialbot assign me as editor

@editorialbot
Copy link
Collaborator Author

Assigned! @mbarzegary is now the editor

@mbarzegary
Copy link

Hi @haniyeka 👋
Considering your field of expertise, would you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

Paper: https://raw.githubusercontent.com/openjournals/joss-papers/joss.05896/joss.05896/10.21105.joss.05896.pdf
Software: https://github.com/cblearn/cblearn

@mbarzegary
Copy link

Hi @ulf1 @sherbold @stsievert 👋
Considering that you have already published your software in JOSS, would any of you be willing to review this submission for JOSS? As you know, we carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

Paper: https://raw.githubusercontent.com/openjournals/joss-papers/joss.05896/joss.05896/10.21105.joss.05896.pdf
Software: https://github.com/cblearn/cblearn

@haniyeka
Copy link

haniyeka commented Dec 4, 2023

Hi @mbarzegary,

Thank you for considering me to review this submission.
Yes, I can do that.

@sherbold
Copy link

sherbold commented Dec 5, 2023

Hi @mbarzegary

While I am generally available as reviewer for JOSS, my schedule is brutal for the rest of this year. If a review in the first half of January is okay, then yes, otherwise I unfortunately need to decline.

@stsievert
Copy link

I'd be happy to review this software. Thanks for the notification.

@mbarzegary
Copy link

Thank you @haniyeka @sherbold @stsievert

@sherbold yes, a review in the first half of January is fine.

@mbarzegary
Copy link

@editorialbot add @haniyeka as reviewer

@editorialbot
Copy link
Collaborator Author

@haniyeka added to the reviewers list!

@mbarzegary
Copy link

@editorialbot add @sherbold as reviewer

@editorialbot
Copy link
Collaborator Author

@sherbold added to the reviewers list!

@mbarzegary
Copy link

@editorialbot add @stsievert as reviewer

@editorialbot
Copy link
Collaborator Author

@stsievert added to the reviewers list!

@mbarzegary
Copy link

@editorialbot start review

@editorialbot
Copy link
Collaborator Author

OK, I've started the review over in #6139.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
pre-review Python TeX Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning waitlisted Submissions in the JOSS backlog due to reduced service mode.
Projects
None yet
Development

No branches or pull requests

6 participants