openml / openml/OpenML

Meaning of immutability

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Description

We once agreed that objects on the OpenML server should be immutable. While this is true for for most parts of all objects that are required to be immutable (datasets can for example not change the name, version, file, and ignore/target attributes) there are inherently things that change on a dataset and a run. For example, a dataset object has a status that can change (active, deactivated) and a run has a set of evaluation measures that can (in theory) be extended over time.

I feel that this is something that is not properly discussed, documented and figured out how to handle this in the workbenches (with cache, e.d.)

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First steps

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  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Review the existing agreement that OpenML objects are immutable, then inventory the dataset fields and run evaluation measures described here alongside mutable status changes. Define and document the rules for these changes and how workbenches should handle cached objects; the issue is complete when those cases and their cache behavior are agreed and recorded.

Written by the indexing model from the issue text.

Assessment

Domain
data, documentation, machine-learning
Issue type
Documentation
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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