8000 Merge pull request #6411 from nelson-liu/create_issue_pr_template · raghavrv/scikit-learn@c60c3f0 · GitHub
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Merge pull request scikit-learn#6411 from nelson-liu/create_issue_pr_template
[MRG+2] DOC: add issue and pull request template
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‎ISSUE_TEMPLATE.md

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Please try to adhere to the guidelines below as much as possible when
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submitting your issue.
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- Verify that your issue is not being currently addressed by other
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[issues](https://github.com/scikit-learn/scikit-learn/issues?q=)
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or [pull requests](https://github.com/scikit-learn/scikit-learn/pulls?q=).
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- If your issue is a usage question or does not potentially require
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changes to the codebase to be solved, then
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[StackOverflow](http://stackoverflow.com/questions/tagged/scikit-learn)
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(using the`[scikit-learn]` tag) or our
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[mailing list](https://lists.sourceforge.net/lists/listinfo/scikit-learn-general)
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may be a better place to bring it up. For more information, see
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[User Questions](http://scikit-learn.org/stable/support.html#user-questions).
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If you are submitting a bug issue:
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- Please include your operating system type and version number, as well
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as your Python, scikit-learn, numpy, and scipy versions. This information
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can be found by runnning the following code snippet:
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```
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import platform; print(platform.platform())
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import sys; print("Python", sys.version)
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import numpy; print("NumPy", numpy.__version__)
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import scipy; print("SciPy", scipy.__version__)
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import sklearn; print("Scikit-Learn", sklearn.__version__)
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```
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- Please be specific about what estimators and/or functions are involved
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and the shape of the data, as appropriate; please include a
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[reproducible](http://stackoverflow.com/help/mcve) code snippet
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or link to a [gist](https://gist.github.com). If an exception is raised,
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please provide the traceback.
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- Please ensure all code snippets and error messages are formatted in
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appropriate code blocks.
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See ["Creating and highlighting code blocks"](https://help.github.com/articles/creating-and-highlighting-code-blocks).
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If you are submitting an algorithm or feature request:
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- Please verify that the algorithm fulfills our
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[new algorithm requirements](http://scikit-learn.org/stable/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published).
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Thanks for contributing! Please delete these guidelines before submitting
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your issue.

‎PULL_REQUEST_TEMPLATE.md

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Please try to adhere to the guidelines below as much as possible when submitting your pull request.
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- Please verify that your code satisfies the
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[code/documentation quality guidelines](http://scikit-learn.org/stable/developers/contributing.html#coding-guidelines).
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- Please prefix the title of your pull request with `[MRG]` if the
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contribution is complete and should be subjected to a detailed review.
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- Incomplete contributions should be prefixed `[WIP]` to indicate a work
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in progress (and changed to `[MRG]` when it matures). WIPs may be useful
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to: indicate you are working on something to avoid duplicated work,
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request broad review of functionality or API, or seek collaborators.
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WIPs often benefit from the inclusion of a
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[task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments)
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in the PR description.
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- Documentation and high-coverage tests are necessary for enhancements to
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be accepted.
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- If you are adding an enhancement, you may wish to provide evidence for
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its benefit with distinguishing examples in the code and benchmarks
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in the PR discussion.
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- If your pull request addresses an issue, please use the title to describe
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the issue and mention the issue number in the pull request description to
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ensure a link is created to the original issue.
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Please be aware that we are a loose team of volunteers so patience is
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necessary; assistance handling other issues is very welcome. We value
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all user contributions, no matter how minor they are. If we are slow to
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review, either the pull request needs some benchmarking, tinkering,
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convincing, etc. or more likely the reviewers are simply busy. In either
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case, we ask for your understanding during the review process.
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For more information, see
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[Why is my PR not getting any attention?](http://scikit-learn.org/dev/faq.html#why-is-my-pull-request-not-getting-any-attention)
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Thanks for contributing! Please delete these guidelines before submitting your pull request.

‎doc/faq.rst

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use of GPUs for efficient computing. However, neither of these fit within
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the design constraints of scikit-learn. As a result, deep learning is
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currently out of scope for what scikit-learn seeks to achieve.
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Why is my pull request not getting any attention?
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-------------------------------------------------
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The scikit-learn review process takes a significant amount of time, and
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contributors should not be discouraged by a lack of activity or review on
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their pull request. We care a lot about getting things right
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the first time, as maintenance and later change comes at a high cost.
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We rarely release any "experimental" code, so all of our contributions
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will be subject to high use immediately and should be of the highest
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quality possible initially.
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Beyond that, scikit-learn is limited in its reviewing bandwidth; many of the
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reviewers and core developers are working on scikit-learn on their own time.
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If a review of your pull request comes slowly, it is likely because the
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reviewers are busy. We ask for your understanding and request that you
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not close your pull request or discontinue your work solely because of
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this reason.

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