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AutoML for Text, Image, and Tabular Data

Project description

AutoML for Text, Image, and Tabular Data

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AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on text, image, and tabular data.

Example

# First install package from terminal:
# python3 -m pip install -U pip
# python3 -m pip install -U setuptools wheel
# python3 -m pip install -U "mxnet<2.0.0"
# python3 -m pip install autogluon  # autogluon==0.2.0

from autogluon.tabular import TabularDataset, TabularPredictor
train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
predictor = TabularPredictor(label='class').fit(train_data, time_limit=120)  # Fit models for 120s
leaderboard = predictor.leaderboard(test_data)
AutoGluon Task Quickstart API
TabularPredictor Quick Start API
TextPredictor Quick Start API
ImagePredictor Quick Start API
ObjectDetector Quick Start API

News

Announcement for previous users: The AutoGluon codebase has been modularized into namespace packages, which means you now only need those dependencies relevant to your prediction task of interest! For example, you can now work with tabular data without having to install dependencies required for AutoGluon's computer vision tasks (and vice versa). Unfortunately this improvement required a minor API change (eg. instead of from autogluon import TabularPrediction, you should now do: from autogluon.tabular import TabularPredictor), for all versions newer than v0.0.15. Documentation/tutorials under the old API may still be viewed for version 0.0.15 which is the last released version under the old API.

Resources

See the AutoGluon Website for documentation and instructions on:

Scientific Publications

Articles

Hands-on Tutorials

Train/Deploy AutoGluon in the Cloud

Citing AutoGluon

If you use AutoGluon in a scientific publication, please cite the following paper:

Erickson, Nick, et al. "AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data." arXiv preprint arXiv:2003.06505 (2020).

BibTeX entry:

@article{agtabular,
  title={AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data},
  author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander},
  journal={arXiv preprint arXiv:2003.06505},
  year={2020}
}

AutoGluon for Hyperparameter and Neural Architecture Search (HNAS)

AutoGluon also provides state-of-the-art tools for neural hyperparameter and architecture search, such as for example ASHA, Hyperband, Bayesian Optimization and BOHB. To get started, checkout the following resources

Also have a look at our paper "Model-based Asynchronous Hyperparameter and Neural Architecture Search" arXiv preprint arXiv:2003.10865 (2020).

@article{abohb,
  title={Model-based Asynchronous Hyperparameter and Neural Architecture Search},
  author={Klein, Aaron and Tiao, Louis and Lienart, Thibaut and Archambeau, Cedric and Seeger, Matthias},
  journal={arXiv preprint arXiv:2003.10865},
  year={2020}
}

AutoGluon for Constrained Hyperparameter Optimization

AutoGluon includes an algorithm for constrained hyperparameter optimization. Check out our paper applying it to optimize model performance under fairness constraints: "Fair Bayesian Optimization", AIES (2021).

@article{fairbo,
  title={Fair Bayesian Optimization},
  author={Perrone, Valerio and Donini, Michele and Zafar, Bilal Muhammad and Schmucker, Robin and Kenthapadi, Krishnaram and Archambeau, Cédric},
  journal={AIES},
  year={2021}
}

License

This library is licensed under the Apache 2.0 License.

Contributing to AutoGluon

We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the Contributing Guide to get started.

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