Borda / FLAML

A fast and lightweight AutoML library.

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A Fast Library for Automated Machine Learning & Tuning


FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner.

  1. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classifcal machine learning models and deep neural networks.
  2. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training and evaluation code).
  3. It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research.

FLAML has a .NET implementation as well from ML.NET Model Builder in Visual Studio 2022. This ML.NET blog describes the improvement brought by FLAML.

Installation

FLAML requires Python version >= 3.6. It can be installed from pip:

pip install flaml

To run the notebook examples, install flaml with the [notebook] option:

pip install flaml[notebook]

Quickstart

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"])
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.
estimator.fit(X_train, y_train)

Documentation

You can find a detailed documentation about FLAML here where you can find the API documentation, use cases and examples.

In addition, you can find:

  • Demo and tutorials of FLAML here.

  • Research around FLAML here.

  • FAQ here.

  • Contributing guide here.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

If you are new to GitHub here is a detailed help source on getting involved with development on GitHub.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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A fast and lightweight AutoML library.

License:MIT License


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