JuliaAI / JuliaAI/MLJTuning.jl
Higher dimensional ranges and nested ranges specification
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- Dominant language
- Julia
- Stars
- 70
- Forks
- 12
- PR merge metrics
- No merged PRs in 30d
Description
One dimensional range in MLJBase, how does that fit with MLJTuning and with the generalisation where you may want to specify “spaces” for sets of parameters.
It might be interesting to see how this is done in other optimisation packages in Julia such as JUMP.
Looking beyond Julia, there MLRMBO which can handle seriously complex parameter spaces see example. MLR3 has a parameter package called paradox - nested conditions can be described as outlined here nested parameter conditions.
Contributor guide
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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 reviewing how one-dimensional ranges are represented in MLJBase and used by MLJTuning, then compare the JUMP, MLRMBO, and paradox examples linked in the issue. Before implementation, define the supported higher-dimensional and nested parameter-space cases and their acceptance criteria; the issue names no repository files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100