To what extent can R-tree be guaranteed to be deterministic?
We're using R-tree in TorchGeo for ML, where reproducibility of experiments and data splits is very important. Currently, we populate an R-tree index with a list of files, then use index.intersection(index.bounds) to iterate over all files. We've had a number of issues in the past with sets/dicts and want to make sure we're using R-tree correctly as well. By determinism, I mean that given the same random seed, all code reproduces the same results exactly.
As far as I can tell, files are returned in insertion order. Is this behavior guaranteed? Are there any other parts of R-tree that may not be deterministic?
To what extent can R-tree be guaranteed to be deterministic?
We're using R-tree in TorchGeo for ML, where reproducibility of experiments and data splits is very important. Currently, we populate an R-tree index with a list of files, then use
index.intersection(index.bounds)to iterate over all files. We've had a number of issues in the past with sets/dicts and want to make sure we're using R-tree correctly as well. By determinism, I mean that given the same random seed, all code reproduces the same results exactly.As far as I can tell, files are returned in insertion order. Is this behavior guaranteed? Are there any other parts of R-tree that may not be deterministic?