Customizing `processing_pipeline` for benchmarking
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- Python
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Description
Hi all. I'm trying to benchmark a few pretrained EEG models, but most of these models have their own required processing pipelines and thus the automatically added band pass would usually need to be removed. I'm currently just using evaluators individually and overriding the processing pipelines according to this guide, but I think giving the user some basic choices for modifying the pipeline used in benchmarking would be beneficial.
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 with the linked pre-processing guide and the evaluator path currently used when pipelines are overridden individually. Trace where benchmarking adds the automatic band-pass and determine what basic user choices are feasible; done means benchmark runs can modify or remove that processing while preserving existing evaluator behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100