Add check for strictly positive training data for CategoricalHMM
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- Dominant language
- Python
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Description
Apologies if I missed this but I wasn't able to find in the documentation the CategoricalHMM takes positive values only. I only realized it when I noticed my predictions from fitted models behaving weirdly.
It would be useful to add a warning (or even a transform of the inputs) when fitting the HMM.
Happy to submit a PR if this would be useful.
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 at the CategoricalHMM fitting entry point and inspect how training data is currently accepted. Confirm whether the expected behavior is a warning or transformation, then add coverage showing how non-positive data is handled and run the relevant test suite.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100