Base XGBoost's shuffle warning on the fit loader, not the dataset flag

[solarsys]

Problem

XGBoostModel.fit warns when its training loader shuffles, because row order changes row subsampling. The check reads loader.dataset.shuffle. Loaders from get_dataloader (_SeededDataLoader) set that flag on the shared dataset at the start of each pass. So after any shuffled loader on the same dataset has run, for example another model's training loader, the flag stays True. fit with get_dataloader(ds, ..., shuffle=False) then warns that the loader shuffles, which is false.

This showed up in the new Tutorial 00, which trains an RNN and XGBoost on the same split. The notebook currently works around it by training XGBoost first.

Fix

Use the loader's own shuffle_samples setting when it has one. That is what applies when the loader iterates. Other loaders keep the old check: a RandomSampler, or the dataset's flag.

Test

test_shuffle_warning_follows_the_fit_loader: after iterating a shuffled loader on the dataset, fitting with an unshuffled loader logs no warning, and fitting with a shuffled loader still warns. It fails without the fix. All 22 tests in tests/core/test_xgboost_model.py pass, ruff is clean, and tools/check_pr_rules.py passes.

🤖 Generated with Claude Code

https://claude.ai/code/session_01PhbzTePoLMBYwjMLG8R9ek

[github-actions]

Test Coverage

Total: 67.87% (16694 / 24597 lines)

Modified file	Coverage	Lines
pyhealth/models/gradient_boosted_trees.py	27.86%	78 / 280

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