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DOC ensures that sklearn.metrics.cluster._supervised.adjusted_rand_score passes numpydoc validation. #24185
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Hey there @zadilkhwaja, thanks for the PR (and sorry for the delay)! Could you update your title as specified in the issue? Specifically:
And you still have a couple errors in the numpydoc validation to resolve, namely:
Please let me know if you have any questions or concerns, thanks! |
Sorry I'm new to opensource and scikit learn. I'm not sure how to resolve these errors. Could you please guide me. |
Sure, I'd be happy to! First off, thank you for updating the title -- that part looks good now! The two errors refer to the current docstring for
This error can be a bit annoying. It means that somewhere there is an unwanted extra (empty) line. So the difference between:
and
As the error says, this is most likely between sections or between paragraphs, however it could also be an extra blank line at then end of the docstring. The second error is a bit simpler:
Sections are denoted with the title underlined by dashes, e.g.
As the error says, there is a correct order for them to follow: If you still have any questions or doubts please let me know! If you end up stuck while trying to solve these, I can take a look and show you the necessary changes. Hope this helps :) |
Rearranged sections and removed extra line from the end of the file.
Hi! Thankyou @Micky774 for helping. I've made some changes pls check. |
Co-authored-by: Arturo Amor <[email protected]>
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@zadilkhwaja - Congrats on your first scikit-learn PR! The code is currently failing the black formatting check – I've commented on the spots that need to be fixed to pass 😄
Thankyou everyone for helping. I think someone already worked on this & merged a PR 3 days ago. |
Indeed, it was solved in #24096, but I am glad this did not discourage you. I am looking forward to seeing your next contribution :) This PR can be safely closed. |
Reference Issues/PRs
Adressess #21350
What does this implement/fix? Explain your changes.
This PR ensures that sklearn.metrics.cluster._supervised.adjusted_rand_score passes numpydoc validation.