Submission: pymlviz (Python)

Submitting Authors: Anas Muhammad (@anasm-17), Fanli Zhou(@flizhou), Tao Huang (@taohuang-ubc), and Mike Chen (@miketianchen) Package Name: pymlviz One-Line Description of Package: Visualize and compare the performance of machine learning models. Repository Link: https://github.com/UBC-MDS/pymlviz Version submitted: 1.1.13 Editor: Varada Kolhatkar (@kvarada ) Reviewer 1: Jacky Ho (@jackyho112 ) Reviewer 2: Manish Joshi (@ManishPJoshi ) Archive: TBD
Version accepted: TBD

Description

Include a brief paragraph describing what your package does:

This package contains four functions to allow users to conveniently plot various visualizations as well as compare the performance of different classifier models. The purpose of this package is to reduce time spent on developing visualizations and comparing models, to speed up the model creation process for data scientists.

Scope

Please indicate which category or categories this package falls under:
- [ ] Data retrieval
- [ ] Data extraction
- [ ] Data munging
- [ ] Data deposition
- [ ] Reproducibility
- [ ] Geospatial
- [ ] Education
- [x] Data visualization*

* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see this section of our guidebook.

Explain how the and why the package falls under these categories (briefly, 1-2 sentences): Enables you to easily visualize ML model performance.
Who is the target audience and what are scientific applications of this package?
Machine learning model developers and data scientists.
Are there other Python packages that accomplish the same thing? If so, how does yours differ? Scikit-learn offers a lot of similar features, like visualizing ROC curves, plotting confusion matrix, however, our package wraps those functions allowing users to implement such visualizations more easily. Moreover, we have made the process of model comparison easier with the help of our package.
If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] has an OSI approved license
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions.
[x] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

Publication options

[ ] Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements](https://joss.readthedocs.io/en/latest/submitting.html#submission-requirements). Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements](https://joss.readthedocs.io/en/latest/submitting.html#submission-requirements): "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements](https://joss.readthedocs.io/en/latest/submitting.html#what-should-my-paper-contain) with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

[x] Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Code of conduct

[x] I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

P.S. Have feedback/comments about our review process? Leave a comment here

Editor and Review Templates

Editor and review templates can be found here

Package Review

Please check off boxes as applicable, and elaborate in the 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 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 setup.py file or elsewhere.

Readme 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
[x] Badges for continuous integration and test coverage, the badge for pyOpenSci peer-review once it has started (see below), a repostatus.org badge, and any other badges. If the README has many more badges, you might want to consider using a table for badges, see this example, that one and that one. Such a table should be more wide than high.
[x] Short description of goals of the package, with descriptive links to all vignettes (rendered, i.e. readable, cf the documentation website section) unless the package is small and there’s only one vignette repeating the README.
[x] Installation instructions
[x] Any additional setup required (authentication tokens, etc)
[x] Brief demonstration usage
[x] Direction to more detailed documentation (e.g. your documentation files or website).
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages
[x] Citation information

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: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
[x] Continuous Integration: Has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.

For packages co-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

Hey folks, good job! Your package works! As far as I can tell. Here is some feedback.

Your online documentation is broken. For instance, the index page is empty and the model index page does not exist.
The model_comparison_table function is a bit limiting. Currently, it only supports the default scoring built-in in Scikit Learn ML models, but in many cases, the user will want to use other scoring methods for different ML projects. Please consider supporting those so that the function can be more useful in various scenarios. In addition, despite the fact that you have mentioned that the function takes in Scikit Learn ML models in the description, please also specify that in the argument description.
The plot_roc function example in the README doc could be misleading. The first argument takes a fit model, but the example, plot_roc(SVC(), X_valid, y_valid), would indicate otherwise. If someone runs that code, that person would run into an error.
The plot_train_valid_error function should take a model instance instead of a name in order to allow passing in non-default hyperparameters to the models as opposed to only one of the allowed ones in the function. This is because, at its current state, the function has much-unrealized potential. Hyperparameters interact with each other, meaning one hyperparameter value may produce a different result with different values of another hyperparameter. Basically, if we are only allowed tuning of one individual hyperparameter, I think the function has extremely limited practicality.
For the plot functions, perhaps you could allow a basic level of customizations for the graphs. For instance, it might be useful to change the graph's size and colors. Passing in these options directly to the plotting method could be quite easily done.