Replace use of list for cached_data and subject_ids in pytorch dataset with polars objects

[juancq]

This fixes the increased memory consumption issues when using multiple pytorch dataloaders (issue #73). It also dropped the starting memory usage in my test case from 30GB to 12GB.

Removing this line makes all the difference:

EventStreamGPT/EventStream/data/pytorch_dataset.py

Line 309 in b10e741

self.cached_data = self.cached_data.rows()

Editing the following line didn't make much of a difference, but I edited it for consistency:

EventStreamGPT/EventStream/data/pytorch_dataset.py

Line 306 in b10e741

self.subject_ids = self.cached_data["subject_id"].to_list()

[mmcdermott]

Hey @juancq -- how does this impact the iteration speed through the dataloader, though? The motivation to convert things to lists was that with raw polars objects, the base iteration speed was much slower.

[juancq]

@mmcdermott I saw no noticeable difference in the iteration speed.

[mmcdermott]

@juancq I'm working on a different solution for this problem that also addresses some other issues. I'll tag you in that other PR. It's not 100% ready but it is close. It is a larger change, but I'll explain more there.