PGijsbers
commented
1 year ago There is currently no way to hide any data from users. The only way to make sure users have absolutely no access to the test labels, is by uploading not uploading it. This means uploading two datasets: one with training data and labels, and one with test data without labels. This will allow the users to easily access these datasets, but still won't allow for uploading predictions of the hidden test set. This is not supported. You would need some alternate solution for submitting and processing these predictions. As far as I am aware, there are currently no plans to support this competition-style hidden data.
Circles are for sharing entire datasets, but only with a set of trusted users. This is currently on our roadmap, but I would not expect if before Fall this year (this is not a promise).
If you want, you can create an OpenML feature request on our OpenML discussion board. Be elaborate, specify use cases, target audience, give examples of it being used, and an example of how you think this would work with OpenML from a user-perspective (in this case, both the competition organizer and the competition participant). This makes it easier for us to discuss the idea and determine if it's something we want to support.
mfeurer
Description
My understanding is that when creating a task, the selected estimation procedure defines the train/test splits of the dataset. When creating a run, a model is evaluated on the given dataset using some estimation procedure such as cross-validation.
Is it possible to provde hidden test sets for evaluation of solutions? There is a "Note" about that on https://openml.github.io/OpenML/#circles-under-construction in the section about Tasks but I'm not sure if this version of the docs is up-to-date.
For my use case, this issue is connected to openml/openml-python#1231. I would like the user to be able to upload the predictions for a test set where the labels are hidden, so the user has no access to it. Is this possible by default? Or would I need to create a separate task with a separate dataset that only contains X, but no labels? What would be the recommende approach here?
Thanks!