v3.0.1

Added

• Hugging Face Hub integration: get_hf_data downloads a Hub dataset split, converts it to pandas, and returns ranking-ready (X, y) given the label column and columns to exclude; load_hf_dataset returns the raw DataFrame; hf_login authenticates for private or gated datasets. datasets >= 2.19 joins the required dependencies.
• Convex combination fitting: RankingResult.fit_convex(X, y, top_n=...) fits the optimal weights >= 0 summing to one over the top consensus features (a deterministic convex quadratic program), and the standalone fit_convex fits every column of X. Features are z-scored internally by default (standardize=False fits the raw values). Both return a ConvexFit with aligned weights, an R2 or ROC AUC fit metric, predict, and table.
• get_data gained encoding and max_categories parameters.

Changed

• get_data now one-hot encodes categorical feature columns by default: each unique value becomes a 0/1 sub-feature named "{column}-{value}" (color-blue for strings, color-0 for integer categories), injected at the parent column's position. Columns with more than max_categories unique values (default 64) fall back to label encoding, and encoding="label" restores the previous behavior. Booleans stay single 0/1 columns.