Group 22 - linear regression with coordinate descent algorithm

[andyzhangstat]

Submitting Author: Andy Zhang @andyzhangstat All current maintainers: (@Jing-19, @fohy24) Package Name: lr_cd One-Line Description of Package: A better implementation of linear regression in Python! Repository Link: https://github.com/UBC-MDS/lr_cd Version submitted: https://github.com/UBC-MDS/lr_cd/releases/tag/v0.3.0 Editor: Tiffany Timbers Reviewer 1: LI Rachel Reviewer 2: Wang Doris Reviewer 3: Mahmood Waleed Archive: TBD JOSS DOI: TBD Version accepted: TBD Date accepted (month/day/year): TBD

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• We implement linear regression using the coordinate descent (CD) algorithm in this Python package. For additional details about the coordinate descent (CD) algorithm, please refer to the link.

Our package consists of three major components: Simulated data generation Coordinate descent algorithm Visualization of data and the fitted linear regression line

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[Rachel0619]

Package Review

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Final approval (post-review)

• [X] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 2 hours

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Review Comments

Overall, the package is well built and documented. Installation succeed as instructed and all the functions run properly without throwing any error. Test coverage are 100%. In particular, you did a really great job when you validate your function with synthetic dataset, diabetes dataset and also scikit-learn, which make your package much more robust and trustworthy.

There are a few things you might want to tweak:

1. You may want to show the results after calling the synthetic data generation function in your readthedocs, i.e show how the generated X and y look like.
2. You may want to change dependencies in the project.toml file prefixed with "^" to ">=" so that your package can work with for example python version 4.0.0 and above.
3. You may want to add badges for Continuous integration and test coverage, python versions supported and repostatus.org
4. You may want to use github issues more often and assign labels to each of them

[MDSFusionist]

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

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Documentation

The package includes all the following forms of documentation:

• [X] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• [X] Installation instructions: for the development version of the package and any non-standard dependencies in README.
• [X] Vignette(s) demonstrating major functionality that runs successfully locally.
• [X] Function Documentation: for all user-facing functions.
• [X] Examples for all user-facing functions.
• [X] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
• [ ] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

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• [X] Package has a README.md file in the root directory.

The README should include, from top to bottom:

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• [ ] Badges for:
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  • [ ] Python versions supported,
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NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

• [X] Short description of package goals.
• [X] Package installation instructions
• [X] Any additional setup required to use the package (authentication tokens, etc.)
• [X] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • [X] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• [X] Link to your documentation website.
• [X] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• [X] Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:

• [X] Package documentation is clear and easy to find and use.
• [X] The need for the package is clear
• [X] All functions have documentation and associated examples for use
• [X] The package is easy to install

Functionality

• [X] Installation: Installation succeeds as documented.
• [X] Functionality: Any functional claims of the software been confirmed.
• [X] Performance: Any performance claims of the software been confirmed.
• [X] Automated tests:
  • [X] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
  • [X] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• [X] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• [ ] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
  • [ ] Package supports modern versions of Python and not End of life versions.
  • [ ] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

• [ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

• [ ] A short summary describing the high-level functionality of the software
• [ ] Authors: A list of authors with their affiliations
• [ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
• [ ] References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

• [ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing:

1.5

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Review Comments

The User Guide is well-written, making it accessible even to those who might be new to linear regression or the Coordinate Descent algorithm. The language is clear, and the step-by-step explanations are highly beneficial for understanding the package's functionality. The inclusion of examples, such as the synthetic data generation and the application on the diabetes dataset, is excellent. These examples not only illustrate the usage of the package but also demonstrate its practical applicability in real-world scenarios. The comparative analysis with Scikit-learn's implementation is a strong point. This not only shows confidence in the reliability of your package but also provides users with a familiar benchmark for understanding the package's performance.

It's evident that a lot of thought and effort went into this. I would like to offer some suggestions that could further enhance your project:

1. In the user examples section, you've done a great job showcasing the output of your package alongside sklearn's for comparison. To further clarify these comparisons, you might consider differentiating the plot titles based on the methods used to generate them. This distinction would make it immediately clear to users which plots were generated by lr_cd and which by sklearn, especially since the visual outputs are similar.
2. The link in the README documentation section for "online documentation" can be updated with the latest one.
3. Adding a Repository Activity Badge: may provide immediate visibility into the activity status of your repository

[WaleedMahmood1]

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

• [x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

• [x] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• [x] Installation instructions: for the development version of the package and any non-standard dependencies in README.
• [x] Vignette(s) demonstrating major functionality that runs successfully locally.
• [x] Function Documentation: for all user-facing functions.
• [ ] Examples for all user-facing functions.
• [x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
• [x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

Readme file requirements The package meets the readme requirements below:

• [x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

• [x] The package name
• [ ] Badges for:
  • [ ] Continuous integration and test coverage,
  • [x] Docs building (if you have a documentation website),
  • [ ] A repostatus.org badge,
  • [ ] Python versions supported,
  • [x] Current package version (on PyPI / Conda).

NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

• [x] Short description of package goals.
• [x] Package installation instructions
• [x] Any additional setup required to use the package (authentication tokens, etc.)
• [x] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • [x] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• [x] Link to your documentation website.
• [x] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• [x] Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:

• [x] Package documentation is clear and easy to find and use.
• [x] The need for the package is clear
• [x] All functions have documentation and associated examples for use
• [x] The package is easy to install

Functionality

• [x] Installation: Installation succeeds as documented.
• [x] Functionality: Any functional claims of the software been confirmed.
• [x] Performance: Any performance claims of the software been confirmed.
• [x] Automated tests:
  • [x] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
  • [x] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• [x] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• [x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
  • [x] Package supports modern versions of Python and not End of life versions.
  • [x] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

• [ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

• [ ] A short summary describing the high-level functionality of the software
• [ ] Authors: A list of authors with their affiliations
• [ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
• [ ] References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

• [ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 1.5 hrs

---

Review Comments

Overall: The project is very well-structured. Also, the installation instructions are as clear as day and super easy to install (I only needed to copy and paste the instructions into my terminal and they ran without a single hitch). All of the function tests also passed locally. The function itself also seems very interesting to implement.

Feedback:

• A better way to show the functionality of the functions might be to illustrate the outputs of each of the functions. For example, perhaps the optimized intercept and coefficients vector obtained from the coordinate_descent function could be shown as well in the example documentation.
• Add in function explanations where the functions are being used, rather than describing all of them together at the start of the documentation. This way the reader can distinguish between each function's purpose and explicitly see the function in action.
• GitHub issues up until Milestone 3 seem to be a little too few. Though, I can see quite a few being added for the next Milestone 4 to address my previous reviewer comments.