tslearn-team / tslearn-team/tslearn
Support for scipy.sparse.COO
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new feature
- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 384
- Avg merge
- 3d 12h
- Merged PRs (30d)
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Description
Hey,
I have lots of very big 3D EHR data that is also very sparse. The upcoming scipy release will feature n-D sparse COO arrays which are useful to store and retrieve big time series data. I was wondering whether you'd be open to single dispatching your implementations to scipy.sparse.COO for potentially tons of memory savings and speedups?
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
No file, test, or entry point is named. Start by reviewing tslearn's array-processing implementations and scipy.sparse.COO's dispatch support, then define the supported operations and memory or speed criteria before determining what tests would demonstrate completion.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100