FLAML - Fast and Lightweight AutoML

FLAML is a Python library designed to automatically produce accurate machine learning models with low computational cost. It frees users from selecting learners and hyperparameters for each learner. It is fast and cheap. The simple and lightweight design makes it easy to extend, such as adding customized learners or metrics. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research. FLAML is easy to use:

• 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 ray-tune style hyperparameter tuning for a custom function.

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

Installation

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

pip install flaml

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

pip install flaml[notebook]

Examples

A basic classification example.

from flaml import AutoML
from sklearn.datasets import load_iris
# Initialize an AutoML instance
automl = AutoML()
# Specify automl goal and constraint
automl_settings = {
    "time_budget": 10,  # in seconds
    "metric": 'accuracy',
    "task": 'classification',
    "log_file_name": "test/iris.log",
}
X_train, y_train = load_iris(return_X_y=True)
# Train with labeled input data
automl.fit(X_train=X_train, y_train=y_train,
                        **automl_settings)
# Predict
print(automl.predict_proba(X_train))
# Export the best model
print(automl.model)

A basic regression example.

from flaml import AutoML
from sklearn.datasets import load_boston
# Initialize an AutoML instance
automl = AutoML()
# Specify automl goal and constraint
automl_settings = {
    "time_budget": 10,  # in seconds
    "metric": 'r2',
    "task": 'regression',
    "log_file_name": "test/boston.log",
}
X_train, y_train = load_boston(return_X_y=True)
# Train with labeled input data
automl.fit(X_train=X_train, y_train=y_train,
                        **automl_settings)
# Predict
print(automl.predict(X_train))
# Export the best model
print(automl.model)

More examples can be found in notebooks.

Documentation

The API documentation is here.

Read more about the hyperparameter optimization methods in FLAML here. They can be used beyond the AutoML context. And they can be used in distributed HPO frameworks such as ray tune or nni.

For more technical details, please check our papers.

• FLAML: A Fast and Lightweight AutoML Library. Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. To appear in MLSys, 2021.

@inproceedings{wang2021flaml,
    title={Frugal Optimization for Cost-related Hyperparameters},
    author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},
    year={2021},
    booktitle={MLSys},
}

• Frugal Optimization for Cost-related Hyperparameters. Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.
• Economical Hyperparameter Optimization With Blended Search Strategy. Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. To appear in ICLR 2021.

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.

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.

Authors

• Chi Wang
• Qingyun Wu

Contributors (alphabetical order): Alex Deng, Silu Huang, John Langford, Amin Saied, Markus Weimer, Haozhe Zhang, Erkang Zhu.

License

MIT License