✅FLAML (Microsoft)

A Fast Library for Automated Machine Learning & Tuning

GitHub - microsoft/FLAML: A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.GitHub

Quickstart

• With three lines of code, you can start using this economical and fast AutoML engine as a scikit-learn style estimator.

from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")

• You can restrict the learners and use FLAML as a fast hyperparameter tuning tool for XGBoost, LightGBM, Random Forest etc. or a customized learner.

automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])

• You can also run generic hyperparameter tuning for a custom function.

from flaml import tune
tune.run(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)

• Zero-shot AutoML allows using the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task.

from flaml.default import LGBMRegressor

# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
estimator = LGBMRegressor()
# The hyperparameters are automatically set according to the training data.py
estimator.fit(X_train, y_train)