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[MRG+2] DOC: add issue and pull request template #6411

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39 changes: 39 additions & 0 deletions ISSUE_TEMPLATE.md
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Please try to adhere to the guidelines below as much as possible when
submitting your issue.
- Verify that your issue is not being currently addressed by other
[issues](https://github.com/scikit-learn/scikit-learn/issues?q=)
or [pull requests](https://github.com/scikit-learn/scikit-learn/pulls?q=).
- If your issue is a usage question or does not potentially require
changes to the codebase to be solved, then
[StackOverflow](http://stackoverflow.com/questions/tagged/scikit-learn)
(using the`[scikit-learn]` tag) or our
[mailing list](https://lists.sourceforge.net/lists/listinfo/scikit-learn-general)
may be a better place to bring it up. For more information, see
[User Questions](http://scikit-learn.org/stable/support.html#user-questions).

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Should we somehow discourage people from asking usage questions that are more suited to a place like StackOverflow? I've come across a number of those in the past.

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Yes, a reminder of distinctions between SO, mailing list and issues would be valuable.

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Rule of thumb: if your issue doesn't (potentially) require changes to the code-base to be solved, then StackOverflow is probably a better place to bring it up.

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Small nitpick: I think it should be ["User Questions"] (https.....). The brackets have been interchanged by mistake I think.

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Wow, that's actually a pretty big error haha. Thanks for the notification :)

If you are submitting a bug issue:
- Please include your operating system type and version number, as well
as your Python, scikit-learn, numpy, and scipy versions. This information
can be found by runnning the following code snippet:
```
import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numpy; print("NumPy", numpy.__version__)
import scipy; print("SciPy", scipy.__version__)
import sklearn; print("Scikit-Learn", sklearn.__version__)
```
- Please be specific about what estimators and/or functions are involved
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Can you add something about giving the full error message including traceback somewhere around here.

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It's mentioned on the next line, I think it fits better there

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fine

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I would add something like somewhere around here:

"The most useful to report a issue is to provide code people can use to reproduce the problem" and link to something like http://stackoverflow.com/help/mcve.

and the shape of the data, as appropriate; please include a
[reproducible](http://stackoverflow.com/help/mcve) code snippet
or link to a [gist](https://gist.github.com). If an exception is raised,
please provide the traceback.
- Please ensure all code snippets and error messages are formatted in
appropriate code blocks.
See ["Creating and highlighting code blocks"](https://help.github.com/articles/creating-and-highlighting-code-blocks).

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Add "Please be specific about what estimators/functions are involved and the shape of the data, as appropriate; you may include code snippets or link to a [gist](https://gist.github.com). If an exception is raised, please provide the traceback."

If you are submitting an algorithm or feature request:
- Please verify that the algorithm fulfills our
[new algorithm requirements](http://scikit-learn.org/stable/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published).

Thanks for contributing! Please delete these guidelines before submitting
your issue.
33 changes: 33 additions & 0 deletions 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.

- Please verify that your code satisfies the
[code/documentation quality guidelines](http://scikit-learn.org/stable/developers/contributing.html#coding-guidelines).
- Please prefix the title of your pull request with `[MRG]` if the
contribution is complete and should be subjected to a detailed review.

- Incomplete contributions should be prefixed `[WIP]` to indicate a work
in progress (and changed to `[MRG]` when it matures). WIPs may be useful
to: indicate you are working on something to avoid duplicated work,
request broad review of functionality or API, or seek collaborators.
WIPs often benefit from the inclusion of a
[task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments)
in the PR description.
- Documentation and high-coverage tests are necessary for enhancements to
be accepted.
- If you are adding an enhancement, you may wish to provide evidence for
its benefit with distinguishing examples in the code and benchmarks
in the PR discussion.
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I would definitely add somewhere that if you are addressing an issue, the title of the PR should be describing the issue, but the text of the PR should have #issuenumber in it, so that a link is created. We often get titles like "fixing #1234" which I don't find very helpful for browsing the pull requests, and also they don't create a link to the issue.

- If your pull request addresses an issue, please use the title to describe
the issue and mention the issue number in the pull request description to
ensure a link is created to the original issue.

Please be aware that we are a loose team of volunteers so patience is
necessary; assistance handling other issues is very welcome. We value
all user contributions, no matter how minor they are. If we are slow to
review, either the pull request needs some benchmarking, tinkering,
convincing, etc. or more likely the reviewers are simply busy. In either
case, we ask for your understanding during the review process.
For more information, see
[Why is my PR not getting any attention?](http://scikit-learn.org/dev/faq.html#why-is-my-pull-request-not-getting-any-attention)

Thanks for contributing! Please delete these guidelines before submitting your pull request.
18 changes: 18 additions & 0 deletions doc/faq.rst
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Expand Up @@ -257,3 +257,21 @@ Deep learning requires a rich vocabulary to define an architecture and the
use of GPUs for efficient computing. However, neither of these fit within
the design constraints of scikit-learn. As a result, deep learning is
currently out of scope for what scikit-learn seeks to achieve.


Why is my pull request not getting any attention?
-------------------------------------------------
The scikit-learn review process takes a significant amount of time, and
contributors should not be discouraged by a lack of activity or review on
their pull request. We care a lot about getting things right
the first time, as maintenance and later change comes at a high cost.
We rarely release any "experimental" code, so all of our contributions
will be subject to high use immediately and should be of the highest
quality possible initially.

Beyond that, scikit-learn is limited in its reviewing bandwidth; many of the
reviewers and core developers are working on scikit-learn on their own time.
If a review of your pull request comes slowly, it is likely because the
reviewers are busy. We ask for your understanding and request that you
not close your pull request or discontinue your work solely because of
this reason.