Integrate with River
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 30/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning, stream-processing
Research direction
No files or tests are named. Start by reviewing existing streamz node APIs and River's online model and pipeline interfaces, then determine how RiverLearn and RiverPredict should share a model and how condition-based emissions should work. Done means the integration supports the proposed node pair and relevant River event types with tests.
Written by the indexing model from the issue text.
Description
https://riverml.xyz/latest/ provides online, per-event machine learning. Whilst we have tried to approach them to accept streamz as a known type of input, it makes sense to just do it here.
The idea would be to have RiverLearn streamz node (either a sink, or a normal node returning a metric) and a RiverPredict node, the pair sharing the same River model or pipeline. The River calls are blocking, but fast - so this can be part of a fast async events system. There are other specific types in River that might emit on a condition, e.g., the drift detector's .change_detected, so we might have a few classes to make, but all of them pretty simple.
- Dominant language
- Python
- Stars
- 1.3k
- Forks
- 149
- Avg merge
- 17h 39m
- Merged PRs (30d)
- 1
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.
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