Please review TanML

[tradertanmay]

Submitting Author: @tradertanmay
All current maintainers:
Package Name: tanml
One-Line Description of Package: Automated Model Validation Toolkit for Tabular Machine Learning
Repository Link: https://github.com/tdlabs-ai/tanml
Version submitted: 0.1.10
EiC: @crhea93
Editor: @crhea93
Reviewer 1: @echen1214
Reviewer 2: @anabeltan
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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  Data validation & testing: TanML automates tabular data checks (profiling, drift) to ensure data quality in ML workflows.

  Workflow automation: TanML streamlines end-to-end model validation and generates audit-ready reports for reproducible governance.

  • Who is the target audience and what are scientific applications of this package?
    Target audience: Data scientists and ML engineers building tabular models, plus model validation/MRM teams who need standardized,
    repeatable checks.

    Scientific applications: Reproducible evaluation of tabular ML models and datasets (data quality, performance, robustness, fairness,
    explainability) with automated report generation for research and audit documentation.

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[crhea93]

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Editor comments

Major Comments:

• The unit tests compile nicely on my local machine, but we require that the submission comes with automated unit tests. The easiest way to do this is to use GitHub Actions to do this. Here is a nice review of how to do this https://docs.github.com/en/actions/tutorials/build-and-test-code/python.

Minor Comments:

• It would be useful to include how to run the pytests and any additional dependencies required for this. I do note that this is mentioned in the contributing tab. If you feel like that is sufficient, then please disregard this comment :)
• I'd suggest considering including linting, tooling, or type checks (see above for more details). These can be very handy in keeping the code well-structured and concise.
• At first glance the documentation looks really good (the reviewers will go in more depth here). However, it was hard to navigate to it. I suggest having it up front and in center on the excellent README.

[tradertanmay]

Hi @crhea93, thank you for the feedback.

Automated Unit Testing

• Implemented a GitHub Actions workflow that automatically runs unit tests on every push.
• The tests run across Python versions 3.10 through 3.13 to ensure compatibility.

Local Testing and Dependencies

• Added a Testing section to the README with instructions for local verification.
• Configured development dependencies so they can be installed with a single command: pip install -e ".[dev]".

Quality Tooling and Type Checks

• Integrated Ruff for automated linting and code formatting in the CI pipeline.
• Implemented Mypy for static type checking across the library.

Documentation Discoverability

• Moved the documentation link to the top of the README for immediate visibility.

Status: All automated checks for linting, type safety, and unit testing are currently passing in the GitHub repository.

[crhea93]

@tradertanmay Fantastic! I'm very interested in this package, so instead of finding an editor, I will go ahead and step in as editor for this submission. I'll start looking for reviewers next week :)

[tradertanmay]

@crhea93 Thank you so much for stepping in as editor. I really appreciate your interest in the package and am excited to move forward. I look forward to working with you and the reviewers.