lmcinnes / lmcinnes/pynndescent
Seeding index with a precalculated KNN graph
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- Dominant language
- Python
- Stars
- 970
- Forks
- 107
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
Sorry if this sounds completely outlandish. This may be somewhat related to #79.
The idea is to provide a pre-calculated graph to the index. And at this 'index seeding' step, a different distance metric than the one used to calculate the seed graph may be used. Thereafter, this graph can be updated with further data batches or/and queried.
Do you think the current implementation of pynndescent makes this feasible? If yes, what would be assumptions about the seed graph that one might need to take care of?
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
Start by reviewing the current pynndescent index construction and update/query entry points to determine whether a pre-calculated KNN graph can be accepted. The issue names no files or tests; done would require a decided design that documents supported seed-graph assumptions and the behavior when its distance metric differs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100