[MRG] Add multiclass support to average_precision_score #17388
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# More detailed explanatory text, if necessary. Wrap it to about 72 # characters or so. In some contexts, the first line is treated as the # subject of the commit and the rest of the text as the body. The # blank line separating the summary from the body is critical (unless # you omit the body entirely); various tools like `log`, `shortlog` # and `rebase` can get confused if you run the two together. # Explain the problem that this commit is solving. Focus on why you # are making this change as opposed to how (the code explains that). # Are there side effects or other unintuitive consequences of this # change? Here's the place to explain them. # Further paragraphs come after blank lines. # - Bullet points are okay, too # - Typically a hyphen or asterisk is used for the bullet, preceded # by a single space, with blank lines in between, but conventions # vary here # If you use an issue tracker, put references to them at the bottom, # like this: # Resolves: scikit-learn#123 # See also: scikit-learn#456, scikit-learn#789
# More detailed explanatory text, if necessary. Wrap it to about 72 # characters or so. In some contexts, the first line is treated as the # subject of the commit and the rest of the text as the body. The # blank line separating the summary from the body is critical (unless # you omit the body entirely); various tools like `log`, `shortlog` # and `rebase` can get confused if you run the two together. # Explain the problem that this commit is solving. Focus on why you # are making this change as opposed to how (the code explains that). # Are there side effects or other unintuitive consequences of this # change? Here's the place to explain them. # Further paragraphs come after blank lines. # - Bullet points are okay, too # - Typically a hyphen or asterisk is used for the bullet, preceded # by a single space, with blank lines in between, but conventions # vary here # If you use an issue tracker, put references to them at the bottom, # like this: # Resolves: scikit-learn#123 # See also: scikit-learn#456, scikit-learn#789
amueller
reviewed
Jun 3, 2020
amueller
reviewed
Jun 3, 2020
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Should be fine to merge @amueller |
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Hi @gbolmier, sorry your pull request got lost. Are you still interested in finishing it? If so, do you mind fixing conflicts? Thanks a lot for your patience! |
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Just wondering, does anyone know when the |
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Thanks @gbolmier for keeping the pull request synchronized. Failures are unrelated to the modifications. Perhaps @lorentzenchr could be interested in a review? Thanks! |
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@gbolmier As the PR has been marked as Stalled, I would like to take over and make the suggested changes. |
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/take |
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Yeah sure @awinml, feel free to take over! From what I remember this PR needed review so I'm not sure it is a priority for the dev team. I would suggest asking them on gitter if it's worth taking over :) |
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Reference Issues/PRs
Fixes #17104
What does this implement/fix? Explain your changes.
Add mean average precision score, the multi-class extension of average precision using OVR.
Any other comments?
average_precision_score handles the multilabel case by averaging each class scores according to the policy defined by the
averageparameter. The multiclass setting being a specific case of the multilabel one, it seems natural to add its support toaverage_precision_score. In an other hand we could also consider splitting the class scores average logic to a different metric namedmean_average_precision_score