Group5 - Autopredictor

[zgarciaj]

Submitting Author: @s-voon All current maintainers: (@AnuBanga , @AReyH , @zgarciaj) Package Name: autopredictor One-Line Description of Package: Python package streamlines the process of selecting and assessing machine learning models, presenting a simplified approach for evaluating different regression models without intricate manual setup. Repository Link: https://github.com/UBC-MDS/autopredictor Version submitted: 0.0.3 Editor: Tiffany Timbers
Reviewer 1: Atabak Alishiri Reviewer 2: Doris Cai
Reviewer 3: Julia Everitt Reviewer 4: Simon Frew JOSS DOI: TBD Version accepted: TBD Date accepted (month/day/year): TBD

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[dorisyycai]

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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:

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Review Comments

Overall, it is a comprehensive and useful package The function tests achieved 100% test coverage, which is impressive The vignette is well written, and the example chosen for demonstration is very helpful for understanding the project. The use of badges in the README allows for easy navigation to various aspects of the package.

Recommendations:

It would be easier to read if the badges are organized in a table format, especially considering their increasing number. It would be great to explicitly list the eight covered models on README, providing users with a quick understanding. Include references or citations that can be referred to, to improve the package's transparency. There's inconsistency on the CI/CD badge on README, showing a failure while the CI/CD action has actually passed. Maybe include how to conduct unit tests and assess test coverage

[srfrew]

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.
• [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.

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The README should include, from top to bottom:

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• [ ] Badges for:
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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
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• [ ] 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.
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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 (NA)

• [ ] 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

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Review Comments

• The project vignette was clear and motivating! Further, the README and package documentation was well written and organized. This package is useful for EDA and selecting baseline model performance. Great work! Below are some recommendations to improve documentation and visuals, and one significant note on display_best_score.
• Installation instructions were overall clear! One note is that there were not instructions to include dev dependencies.
• I recommend that the models types to be trained are listed in the README and documentation of fit. It would likely be good to give a brief overview of the models themselves to orient the reader.
• Does this project require a continuous response or can it handle categoricals? This may be worth noting.
• I recommend that badges are organized into a table (visual preference)
• Significant: The function display_best_score returns the max value, which is incorrect for MAPE, MAE, MSE, and RMSE. It should be the minimum value in these cases, and maximum for R^2.

[atabak-alishiri]

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.
• [X] 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:
  • [X] Continuous integration and test coverage,
  • [X] Docs building (if you have a documentation website),
  • [ ] A repostatus.org badge,
  • [X] Python versions supported,
  • [ ] 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.)
• [ ] 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

---

Review Comments

The package is a robust tool designed to simplify predictive modeling by automating model selection and prediction tasks. Its comprehensive functionality and high test coverage are commendable, reflecting a strong commitment to quality and reliability. I really enjoyed reviewing the package.

Strengths:

• Versatility: Supports a wide range of models, making it suitable for diverse data science projects.
• Documentation: Well-written README and vignettes ensure ease of use, facilitating quick adoption by new users.
• Test Coverage: Impressive 100% test coverage guarantees stability and reduces the risk of bugs.

Recommendations:

• Badge Organization: Organizing badges in a table could enhance the README's readability and visual appeal.
• Model Listings: Clearly listing supported models in the README would immediately inform users about the package's capabilities.
• References: Including references or citations would enhance transparency and provide valuable insights into the methodologies implemented.
• CI/CD Badge Accuracy: Ensuring the CI/CD badge accurately reflects the current build status is essential for maintaining user trust.
• Response Variable Types: Specifying which types of response variables (continuous, categorical) the package can handle would clarify its applicability for potential users.
• GitHub "About" Section: Adding a brief description and links to the documents in the GitHub "About" area can make it easier for people to find and get to know what the package is for and how to use it.

In summary, the autopredictor package is a valuable asset in the data science toolkit, with its benefits significantly outweighing the minor areas for improvement. Addressing these recommendations could further enhance its usability and adoption. Well done fellas.

[juliaeveritt]

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.
• [] 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.
• [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:
  • [x] Continuous integration and test coverage,
  • [x] Docs building (if you have a documentation website),
  • [ ] A repostatus.org badge,
  • [x] 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 hour

Review Comments

• ReadMe: Documentation was easy to follow and clearly outlines the need for the package
• Installation Installation was easy to follow and all tests passed!
• ReadMe: The custom logo is a nice touch!
• ReadMe: It would be useful to add more information about the 8 models in your documentation
• ReadMe: I would also find it helpful to see more specific criteria on input data format in your docs (ie explicitly defining clean data)

Overall, really well done! Everything is well organized and easy to use, and my main critique is adding more information about the specifics of your functions and their outputs to your docs. :)