QuadratiK Submission

[rmj3197]

Submitting Author: Raktim Mukhopadhyay (@rmj3197)
All current maintainers: @giovsaraceno
Package Name: QuadratiK
One-Line Description of Package: QuadratiK includes test for multivariate normality, test for uniformity on the sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson kernel-based density and clustering algorithm for spherical data.
Repository Link: https://github.com/rmj3197/QuadratiK
Version submitted: 1.1.0
EIC: @Batalex
Editor: @isabelizimm
Reviewer 1: @acolum
Reviewer 2: @ab93
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

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  Documentation link : https://quadratik.readthedocs.io/en/latest/

We introduce the QuadratiK package that incorporates innovative data analysis methodologies. The presented software, implemented in both R and Python, offers a comprehensive set of novel goodness-of-fit tests and clustering techniques using kernel-based quadratic distances. Our software implements one, two and k-sample tests for goodness of fit, providing an efficient and mathematically sound way to assess the fit of probability distributions. Expanded capabilities of our software include supporting tests for uniformity on the $d$-dimensional Sphere based on Poisson kernel densities, and algorithms for generating random samples from Poisson kernel densities. Particularly noteworthy is the incorporation of a unique clustering algorithm specifically tailored for spherical data that leverages a mixture of Poisson kernel-based densities on the sphere. Alongside this, our software includes additional graphical functions, aiding the users in validating, as well as visualizing and representing clustering results. This enhances interpretability and usability of the analysis. In summary, our R and Python packages serve as a powerful suite of tools, offering researchers and practitioners the means to delve deeper into their data, draw robust inference, and conduct potentially impactful analyses and inference across a wide array of disciplines.

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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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  • Who is the target audience and what are scientific applications of this package?

    • The QuadratiK package offers robust tools for goodness-of-fit testing, a fundamental aspect in statistical analysis, where accurately assessing the fit of probability distributions is essential. This is especially critical in research domains where model accuracy has direct implications on conclusions and further research directions.
      • Spherical data structures are common in fields such as biology, geosciences and astronomy, where data points are naturally mapped to a sphere. QuadratiK provides a tailored approach to effectively handle and interpret these data.
      • This package is also of particular interest to professionals in health and biological sciences, where understanding and interpreting spherical data can be crucial in studies ranging from molecular biology to epidemiology and public health.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?

    • SciPy and hyppo also have collections of goodness-of-fit test functionalities. Our interest focuses on tests that are based on the family of kernel-based quadratic distances. The kernels we use are diffusion kernels, that is, probability distributions that depend on a tuning parameter and satisfy the convolution property. We also implement the Poisson kernel-based tests for uniformity on the d-dimensional sphere.

    • We are aware of only a limited number of Python libraries that offer spherical clustering capabilities, such as spherecluster (last updated in November 2018) and soyclustering (last updated in May 2020). spherecluster implements Spherical K-Means and clustering using von Mises Fisher distributions as proposed in "Banerjee, Arindam, et al. "Clustering on the Unit Hypersphere using von Mises-Fisher Distributions." Journal of Machine Learning Research 6.9 (2005).". soyclustering implements spherical k-means for document clustering which has been proposed in Kim, Hyunjoong, Han Kyul Kim, and Sungzoon Cho. "Improving spherical k-means for document clustering: Fast initialization, sparse centroid projection, and efficient cluster labeling." Expert Systems with Applications 150 (2020): 113288.

    • In summary, there are fundamental differences between QuadratiK and existing packages that are as follows -

      • The GOF tests are U-statistics based on centered kernels. The concept and methodology of centering is unique to our methods and is not part of the methods appearing in existing packages.
      • An algorithm for connecting the tuning parameter with the statistical properties of the test, namely power and degrees of freedom (DOF) is provided. This feature differentiates our novel methods from methods in other packages.
      • A new clustering algorithm for data that reside on the sphere using the Poisson kernel-based densities is offered. This aspect is not a feature of the existing packages.
      • We also offer algorithms for generating random samples from Poisson kernel-based densities. This capability is also unique to our package.

    • We also implement a GUI to enable interaction with the software in a non-programmatic manner using the streamlit library. We have not found any python package that implements a GUI for the above described tasks.

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    Pre-submission Inquiry for QuadratiK #168

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[Batalex]

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Editor comments

Nice submission, I'll get started on finding the perfect editor for Quadratik!

[Batalex]

Hey @rmj3197,
I am super excited to introduce @isabelizimm as the editor for this submission! Isabel will be your privileged point of contact from now on, though you are welcome to ask me anything during the process.
Please note that she will not get started until the week after June 7th.

Happy reviewing!

[isabelizimm]

Hello there! Happy to be ushering this package through 👋 I'm going to go ahead and start looking for reviewers; I'll plan to touch base when I have reviewers lined up OR in 2 weeks (say, June 24), whichever comes first.

[rmj3197]

Hello @isabelizimm,

Thank you so much for the update and for taking the time to review our package. I look forward to hearing from you soon.

[isabelizimm]

Checking in! I have one reviewer ready (yay!) and have reached out to some possibilities for a second. I'll keep you updated when I know more 👍

[rmj3197]

Hello @isabelizimm , thank you very much for the update!

[isabelizimm]

Welcome welcome to our fearless reviewers: @acolum and @ab93 👋 Thank you SO MUCH for volunteering to review for pyOpenSci! You are two people with awesome math-y, stats-y, ML-y, Python-y backgrounds, which is perfect for this package, and I am looking forward to learning from you through this review process 🌻

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Reviewers: @acolum and @ab93
Due date [NOTE: deadline extended]: August 2

[acolum]

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Estimated hours spent reviewing: 2.5

---

Review Comments

Overall, this submission was well done and followed most Python package development and documentation best practices. I found no major issues with the package's documentation, usability, and functionality, but I've outlined a few minor issues below.

Potential issues that could be fixed:

• The README file in the root directory of the package's repository has a .rst file extension instead of a .md file extension. Since I'm a first-time reviewer, I'm not sure how important this difference is to pyOpenSci.
• This wouldn't be required for final approval, but I think it would be helpful for potential users of your software to list similar packages in the R and Python scientific ecosystems in the README file. For example, what other packages implement similar methods or have similar overall functionality?

Minor issues that need fixing:

• The README file is missing a repo status badge. With the addition of this badge (and potentially others, like the pyOpenSci peer-review badge, in the future), I'd recommend organizing the badges in your README like the badges are organized in this rOpenSci package's README.
• Although the README file links to the primary vignette with examples of how to use the package, the other vignettes in the documentation should be linked to here as well.
• Currently, there are a few citations in the bibliography in the README file, but it's not clear which should be cited when citing the software package. More clarity and/or a BibTeX citation is needed. This guidance from GitHub may help.

[ab93]

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

• 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:

• A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• 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.
• Function Documentation: for all user-facing functions.
• Examples for all user-facing functions.
• 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.

Readme file requirements
The package meets the readme requirements below:

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

The README should include, from top to bottom:

• The package name
• Badges for:
  • Continuous integration and test coverage,
  • Docs building (if you have a documentation website),
  • A repostatus.org badge,
  • 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.)

• Short description of package goals.
• Package installation instructions
• 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.
  • Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• Link to your documentation website.
• If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• 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:

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

Functionality

• Installation: Installation succeeds as documented.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• Automated tests:
  • 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.
  • Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• 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: 3 hours

---

Review Comments

Great submission overall. Documentation is good, and I like the user guide as well.
There are a few tweaks, suggestions and adjustments that I can add here.

Packaging and CI

• The package uses the Poetry package manager, which is great. As a result, it would be nice to include a small guide to install the package for development using poetry.
  e.g. poetry install
• In the package dependencies the Python version requirement is given as a broad python = "^3.9, !=3.9.7".
  This can cause some downstream applications using this package to break if any of its dependencies do not support a new Python version.
  So my recommendation will be to only allow Python versions that the package supports, i.e. something like
  python = ">=3.9, <3.13"
• It would be great to have some linting tools in the CI pipeline. Ruff is a great option, and will eliminate any anti-patterns in the coding practices
• I see the Black format badge, but I don't see the CI having the black format check. Adding that would be great

Code Practices, which again can be identified using a linter like Ruff

• Python typing is missing in the code, and would be a very nice to have, e.g. _pkbc.py
• Raising the base Exception class is not the best practice, a more fine-grained exception raising would be great
• A good practice is also to make sure all instance variables are first defined in the __init__() function, e.g. in the PKBC class,
  self.dat only gets initialized in fit()
• Adding __slots__ would be nice, as it reduces memory footprint

[isabelizimm]

Thank you so much to our reviewers @acolum and @ab93 for your thoughts on QuadratiK!🌷 The next step here is for the author to implement the changes suggested by reviewers. This piece can involve a bit of back and forth, @rmj3197, please let us know in this thread if you have questions about the review. Otherwise, post here when the reviews have been addressed and the reviewers will look over the updates and give their final approval!

.rst file extension instead of a .md file extension.

This is okay! As long as there is a README file there, we are good to go 😄

[rmj3197]

Thank you @acolum and @ab93 for your valuable suggestions and comments. Thank you @isabelizimm for your help and communication. I will address the changes and update you once they are completed. Thank you all for your time.