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
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.
* 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.
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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
Description
sktidy
is a python package that returns a tidy summary output tosklearn
LinearRegression and KMeans models using the functionstidy_lr()
andtidy_kmeans()
. It also outputs the predictions of the model for the original data using the functionsaugment_lr()
andaugment_kmeans()
for LinearRegression and KMeans respectively.Scope
* 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):
Who is the target audience and what are scientific applications of this package?
Are there other Python packages that accomplish the same thing? If so, how does yours differ?
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:
Publication options
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*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.
Code of conduct
P.S. *Have feedback/comments about our review process? Leave a comment here
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