Review request template

[anodaini]

Submitting Author: Jacob McFarlane @JacobMcFarlane, Asma Al-Odaini (@anodaini), Xudong Yang @xudongyang2, Heidi Ye @heidi-ye Package Name: sktidy One-Line Description of Package: Tidy model output for sklearn's LogisticRegression and KMeans Repository Link: sktidy Version submitted: 0.1.1 Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD

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Description

sktidy is a python package that returns a tidy summary output to sklearn LinearRegression and KMeans models using the functions tidy_lr() and tidy_kmeans(). It also outputs the predictions of the model for the original data using the functions augment_lr() and augment_kmeans() for LinearRegression and KMeans respectively.

Scope

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

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

• Explain how the and why the package falls under these categories (briefly, 1-2 sentences):

  The package falls under data extraction because it retrieves data from the models and presents it in a structured way. In addition, it falls under data munging because it transforms the data into a more accessible and appropriate form.

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

  The target audience would be people that want to use sklearn models and have one package that outputs model results in a tidy format similar to who the R package broom.

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

  There are not current packages that implement the same thing. pybroom implements a similar functionality but not for sklearn models.

• 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.
• [ ] 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][JossSubmissionRequirements]. 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][JossSubmissionRequirements]: "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][JossPaperRequirements] 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*

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• [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.

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[anodaini]

@JacobMcFarlane @xudongyang2 @heidi-ye If you guys are ok with this, I can go ahead and post it in the review repo.

[heidi-ye]

Looks good to me!

[xudong-Y]

Good with me!