Top-K precision/recall multilabel metrics for ranking task
Open
Nobody has claimed this yet.
enhancement
help wanted
module: metrics
- Dominant language
- Python
- Stars
- 4.8k
- Forks
- 726
- Avg merge
- 5d 21h
- Merged PRs (30d)
- 5
Description
Following the discussion from https://github.com/pytorch/ignite/issues/466#issuecomment-478339986 it would be nice to have such metric in Ignite.
In the context of a multilabel task, compute a top-k precision/recall per label (treating all labels independently).
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files or tests. Start by reading the linked discussion in issue #466, especially the referenced comment, then inspect Ignite's existing metric implementations. Done means a multilabel ranking metric computes top-k precision and recall independently for every label.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 50/100