chimeraboost 0.8.0

CatBoost-inspired gradient boosting in pure Python with a numba backend

chimeraboost

What if CatBoost, but 30x faster, slightly worse, and all in Python?

⚠️ Project is in active development: breaking changes should be expected.

• Installation

pip install chimeraboost

• Sample code:

from chimeraboost import ChimeraBoostClassifier, ChimeraBoostRegressor

# classification
clf = ChimeraBoostClassifier(early_stopping=True)
clf.fit(X, y, cat_features=[0, 1], sample_weight=w)
proba = clf.predict_proba(X_test)

# regression (RMSE, MAE, or Quantile)
reg = ChimeraBoostRegressor(loss="Quantile", alpha=0.9, early_stopping=True)
reg.fit(X, y)

_{ChimeraBoostEns10 is ChimeraBoost bagged with 10 base gradient boosters.}

• Reproduce the benchmark

python benchmarks/run_benchmarks.py --openml --seeds 5 --save --models ChimeraBoost ChimeraBoostEns10 sklearn_HGB CatBoost LightGBM

• What?

  • Exceedingly opinionated GBDT library that only depends on common Python libraries
    • Accepts categorical features, with catboost-like feature processing
    • Bagging as a first-class feature
    • Automatic early stopping, with automatic grouped splitting for the validation set available
  • Supports regression, quantile regression, binary and multiclass classification.
  • Categorical features, sample weights, and automatic early stopping
  • Within ~3% F1 / ~5% RMSE of CatBoost on a 34-dataset OpenML benchmark, at ~30× the speed

• Why?

  • I want to be able to modify my GBDT library at will
  • I know Python and I don't know C