rfgboost

Gradient boosting with asynchronous random forests, implemented in Rust with Python bindings.

Installation

pip install rfgboost

From source (requires Rust toolchain):

pip install maturin
maturin develop --release

In the browser (Pyodide / JupyterLite)

import micropip
await micropip.install("rfgboost")

Requires Pyodide/JupyterLite ≥ 0.29. rfgboost ships a WASM wheel tagged pyemscripten_2025_0_wasm32 (the only wasm platform tag PyPI accepts); micropip gained support for that tag in Pyodide 0.29, so on older runtimes (0.28.x) micropip.install("rfgboost") fails with "Can't find a pure Python 3 wheel." The categorical/WOE path (WoeEncoder) is unavailable in-browser — fastwoe-rs has no WASM wheel — but the numeric RFGBoost* estimators work.

Quick Start

from rfgboost import RFGBoostClassifier, RFGBoostRegressor

# Classification
clf = RFGBoostClassifier(n_estimators=20, rf_n_estimators=50, rf_max_depth=6)
clf.fit(X_train, y_train)
proba = clf.predict_proba(X_test)
ci = clf.predict_ci(X_test)  # Wilson score intervals

# Regression
reg = RFGBoostRegressor(n_estimators=20, rf_n_estimators=50, rf_max_depth=6)
reg.fit(X_train, y_train)
pred = reg.predict(X_test)
ci = reg.predict_ci(X_test)  # Split conformal prediction intervals

# Async mode (adaptive early stopping via CI convergence)
clf = RFGBoostClassifier(async_mode=True, tol=0.0)

# Categorical features (WOE encoding via fastwoe-rs)
clf = RFGBoostClassifier(cat_features=[0, 1, 2])

Components

Key Features

• Async tree building: Rayon work-stealing with AtomicBool convergence flag. Unstarted trees skip once the ensemble converges.
• CI-based stopping: Wilson intervals (classification) and normal CI (regression) determine convergence automatically with tol=0.
• Histogram splitting: 256-bin quantile histograms for O(n + bins) split search.
• Conformal prediction: Split conformal CIs for regression with coverage guarantees.
• Unsupervised RF: Proximity matrix, outlier detection, MDS embedding, feature importance from Breiman's original method.
• Exact TreeSHAP: Matches the official SHAP package to machine precision.

License

MIT