rfgboost 0.1.1

RFGBoost: async Random Forest + gradient boosting engine in Rust with CI-based convergence

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

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

Class	Description
RFGBoostClassifier	Gradient boosting with RF base learners (binary + multiclass)
RFGBoostRegressor	Gradient boosting with RF base learners (regression)
RandomForestClassifier	Standalone random forest classifier
RandomForestRegressor	Standalone random forest regressor
RandomForestUnsupervised	Breiman's unsupervised RF (proximity, outliers, MDS)
DecisionTree	Single decision tree (exact sklearn match)
TreeSHAP	Exact tree-path-dependent SHAP values

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