Trusted-AI / Trusted-AI/AIX360
Ripper rule induction algorithm treats timestamp type features as categorical
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- Dominant language
- Python
- Stars
- 1.8k
- Forks
- 327
- Avg merge
- 1h 13m
- Merged PRs (30d)
- 1
Description
Ripper algorithm recognizes timestamp features (e.g. 2022-06-14-19.39.35.929641) as integers, and thus encodes them to categorical features. The resulting rules are in terms of equality predicates (e.g. timestamp == 2022-06-14-19.39.35.929641) instead of intervals/inequalities as one would expect.
Proper timestamp type support for Ripper would be nice.
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 Ripper algorithm entry point and tracing how timestamp features are recognized and encoded. Confirm the current equality-based rules, then define completion as timestamp features producing interval or inequality predicates instead; add or run focused coverage for the reported timestamp example.
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
- 35/100