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[MNT] merge `openml-sklearn` extension back into `openml`

Abierto
#1,532 14 comentarios 1 reacción 0 asignados Ver en GitHub

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Refactorización
Claridad
Necesita aclaración
Estado de actividad
Estancado
Stack tecnológico
python
Área
machine-learning

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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-sklearn into openml-python
  • use scikit-base _check_soft_dependencies, or "move into class/function" patterns, to isolate the scikit-learn dependency in the extension and the tests of the extension
  • move scikit-learn completely 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).

Lenguaje dominante
Python
Estrellas
361
Forks
296
Sin PR fusionados en 30 d

Guía de contribución

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Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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