Object storage sizes

[ugroempi]

I have been working with an interaction forest (intaus) on 2000 observations that consists of the default 20000 trees (from package diversityForest). This forest uses 306 682 KB disk space. I have applied Interaction$new and FeatureEffects$new to that forest and stored the resulting objects on disk (R work spaces with a single object each). I end up with the following stored object sizes:

hilf <- Predictor$new(intaus, data=as.data.frame(yx2[,-1]), y=yx2[,1], predict.function=predfun)
hilf2 <- Interaction$new(hilf)
## storage size is 1 227 232 KB
fes <- FeatureEffects$new(hilf)
## storage size is 1 248 226 KB

To me, these sizes appear excessive. I wonder what functionalities of these objects I might miss that justify these huge object sizes. Or would it perhaps be possible for Interaction$new and FeatureEffects$new to return smaller objects without sacrificing functionality?

Best, Ulrike

[christophM]

What is your use case for storing these objects?

One reason for the size is that the Predictor is part of Interaction / FeatureEffects. But it seems not completely explanatory for the size, maybe it is stored more than once.

[ugroempi]

The use case is that I don't want to invest the run time again, and want to have them available later e.g. for plotting or printing in comparison to other numbers calculated elsewhere.

[christophM]

I have not tried it yet, but you could try setting the predictor to NULL: interaction_object$predictor = NULL This should make the object a lot smaller. The results are stored in a data.frame in $results and the plotting should not be affected by it either. It's a hacky solution, so I can't guarantee it works right away

[ugroempi]

Thank you for the proposal. After setting the $predictor to NULL, the file size was only 252 kB. The plot method still works, the print method doesn't (but I can of course access the $results nevertheless).

I think that it would be highly desirable that output objects for interactions and feature effects are more parsimonious per default (green ML!).

By the way, from within R I found it quite difficult to assess object sizes. object.size(hilf2) returned size 448 Bytes(!) for the huge object. That size remains unchanged after removing $predictor.