I am running into the curious case where a fitted model stack retains 0 candidates. I can't replicate this case with dummy data and it is only happening for a specific subset of my training set while others work fine (indicating that this is not a coding issue).
This has me wondering, in what circumstances could this happen?
I find this quite curious as well!
Whatever circumstances this would happen in, I would consider a bug—it's hard to diagnose without a reprex, though.
This may be a bug in the model_stack print method—the print method may say there are no members when there indeed are. Is the resulting object with "0 members" still able to return predictions after fit_members? Are there entries in it's model_stack["member_fits"] slot?
Some other thoughts...
I would then look at the data stack being inputted to blend_predictions(). How many columns does it have? Does the first column contain the true assessment set values, and do the entries in remaining columns look like plausible predictions from the candidate models?
If so, I would then look to the penalty argument in blend_predictions(). What happens if you set this value to 0? This should prevent the meta-learner from discarding any candidates.
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