[MNT] merge `openml-sklearn` extension back into `openml`
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评估
- 难度
- 5/5
- 预计耗时
- 一周以上
- 新手友好度
- 25/100
- Issue 类型
- 重构
- 描述清晰度
- 需要澄清
- 活跃度
- 停滞
- 技术栈
- python
调研方向
Start by comparing the openml-sklearn extension with the openml-python package and reviewing their release and maintenance workflows. Evaluate the proposed merge while preserving scikit-learn dependency isolation through soft dependencies or class/function-level patterns. Done means the extension is merged into openml-python without making scikit-learn a hard dependency for unrelated functionality.
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描述
openml-sklearn is in unmaintained - likely due to splitting packages always dramatically increases maintenance surface and complexity. Once release and maintenance workflows are "forgotten", it leads to abandoment.
Of course there are reasonable rationales behind splitting packages, such as dependency isolation.
My recommendation would be to merge openml-sklearn back into openml, in order to minimize maintenance surface - but also maintain dependency isolation.
My concrete recommendations would be:
- plain merge of
openml-sklearnintoopenml-python - use
scikit-base_check_soft_dependencies, or "move into class/function" patterns, to isolate thescikit-learndependency in the extension and the tests of the extension - move
scikit-learncompletely to a soft dependency set, e.g.,openml-integrations
FYI @joaquinvanschoren, @PGijsbers - what do you think? I think we need to avoid abandonment due to too complex maintenance. Dependency isolation can be maintained by using proper patterns within a single package (not just repository).
- 主要语言
- Python
- 星标
- 361
- 派生
- 296
- PR 合并指标
- 30 天内没有已合并 PR
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