pymc-devs / pymc-devs/pytensor
Implement equivalent numpy median and quantile / percentile
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 644
- Forks
- 208
- Avg merge
- 2d 14h
- Merged PRs (30d)
- 16
Description
Please describe the purpose of filing this issue
Equivalent symbolic methods to those are missing.
The Numpy quantile and percentile methods have too many options for the interpolation argument, and these are planned to be deprecated for a while now (see https://github.com/numpy/numpy/issues/10736). It should suffice to implement the default "linear" interpolation.
I am confident that these should not require any extra Ops.
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 locating the existing symbolic statistical methods and the NumPy compatibility tests or entry points that cover related array operations. Implement equivalent median and quantile/percentile behavior using NumPy's default linear interpolation, then verify the supported cases and confirm that no additional Ops are required.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100