v3.0.4

Added

• Split-aware Hub loading: get_hf_data now resolves a dataset's own validation (validation/valid/val/dev) and test splits automatically (valid_split/test_split, "auto" by default; name explicitly or disable with None). When extra splits exist the return is a DataSplits whose frames were cleaned and encoded jointly, so feature columns always match across splits (a category seen only in test still gets its sub-feature everywhere); rows are cleaned per split and n_rows samples train only.
• Held-out evaluation for convex fits: fit_convex and RankingResult.fit_convex accept valid=(X, y) and test=(X, y) pairs they never train on (e.g. splits.valid, splits.test). Eval targets are encoded with the fitting target's class mapping; unseen labels raise. Best practice: fit on train, choose settings on valid, quote test.

Changed

• ConvexFit.metrics reports the AUC/R2 per split ("train", plus "valid"/"test" when given); metric_value became a property over it and method_metrics now maps each selection to its per-split metrics.
• get_data keeps the original row index of surviving rows instead of resetting it.