fastogb

fastogb is a NumPy-first package for constructing additive rule ensembles. It provides configurable query-selection objectives, search algorithms, losses and weight-update methods, with mandatory Numba CPU acceleration and optional CUDA acceleration for supported NVIDIA systems.

Install the current release from PyPI with:

pip install fastogb

The main public estimator is GeneralRuleBoostingEstimator. Its complete constructor, nested configuration, methods and fitted attributes are described in the GeneralRuleBoostingEstimator interface.

Package maintainers can follow the release guide for TestPyPI and production PyPI uploads.

from fastogb import (FullyCorrective, GeneralRuleBoostingEstimator, OrthogonalBoostingObjective,
                     load_csv)

data, target, feature_names, categorical = load_csv(
    'data.csv', target_name='target', target_map={'negative': -1.0, 'positive': 1.0})
model = GeneralRuleBoostingEstimator(
    num_rules=10, objective_function=OrthogonalBoostingObjective,
    weight_update_method=FullyCorrective(), loss='logistic', search='greedy', n_jobs=4,
    search_params={'feature_names': feature_names, 'categorical': categorical},
    objective_params={'epsilon': 1e-4})
model.fit(data, target)

print(model.rules_)
predictions = model.predict(data)
probabilities = model.predict_proba(data)