Skip to main content

AutoML for Image, Text, and Tabular Data

Project description

AutoML for Image, Text, Time Series, and Tabular Data

Latest Release Continuous Integration Platform Tests Broken Link Checker Python Versions GitHub license Downloads Twitter

Install Instructions | Documentation (Stable | Latest)

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 image, text, time series, and tabular data.

Example

# First install package from terminal:
# pip install -U pip
# pip install -U setuptools wheel
# pip install autogluon  # autogluon==0.8.2

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
MultiModalPredictor Quick Start API
TimeSeriesPredictor Quick Start API

Resources

See the AutoGluon Website for documentation and instructions on:

Refer to the AutoGluon Roadmap for details on upcoming features and releases.

Scientific Publications

Articles

Hands-on Tutorials

Train/Deploy AutoGluon in the Cloud

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.

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}
}

If you are using AutoGluon Tabular's model distillation functionality, please cite the following paper:

Fakoor, Rasool, et al. "Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation." Advances in Neural Information Processing Systems 33 (2020).

BibTeX entry:

@article{agtabulardistill,
  title={Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation},
  author={Fakoor, Rasool and Mueller, Jonas W and Erickson, Nick and Chaudhari, Pratik and Smola, Alexander J},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

If you use AutoGluon's multimodal text+tabular functionality in a scientific publication, please cite the following paper:

Shi, Xingjian, et al. "Multimodal AutoML on Structured Tables with Text Fields." 8th ICML Workshop on Automated Machine Learning (AutoML). 2021.

BibTeX entry:

@inproceedings{agmultimodaltext,
  title={Multimodal AutoML on Structured Tables with Text Fields},
  author={Shi, Xingjian and Mueller, Jonas and Erickson, Nick and Li, Mu and Smola, Alex},
  booktitle={8th ICML Workshop on Automated Machine Learning (AutoML)},
  year={2021}
}

If you use AutoGluon's time series forecasting functionality in a scientific publication, please cite the following paper:

@inproceedings{agtimeseries,
  title={{AutoGluon-TimeSeries}: {AutoML} for Probabilistic Time Series Forecasting},
  author={Shchur, Oleksandr and Turkmen, Caner and Erickson, Nick and Shen, Huibin and Shirkov, Alexander and Hu, Tony and Wang, Yuyang},
  booktitle={International Conference on Automated Machine Learning},
  year={2023}
}

AutoGluon for Hyperparameter Optimization

AutoGluon's state-of-the-art tools for hyperparameter optimization, such as ASHA, Hyperband, Bayesian Optimization and BOHB have moved to the stand-alone package syne-tune.

To learn more, checkout 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}
}

License

This library is licensed under the Apache 2.0 License.

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autogluon.features-0.8.3b20230906.tar.gz (47.0 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file autogluon.features-0.8.3b20230906.tar.gz.

File metadata

File hashes

Hashes for autogluon.features-0.8.3b20230906.tar.gz
Algorithm Hash digest
SHA256 f6e0c700ae510e01a3b788c5d95ef3c8c91f1a48d9e3dbee1fe839378b85326e
MD5 98918835596f8e784c858abf6041e8dc
BLAKE2b-256 78cea452f8ee158964e77bb4a84163deba3089bccf3d1783bc632d56cfa6df53

See more details on using hashes here.

File details

Details for the file autogluon.features-0.8.3b20230906-py3-none-any.whl.

File metadata

File hashes

Hashes for autogluon.features-0.8.3b20230906-py3-none-any.whl
Algorithm Hash digest
SHA256 f0878101ae7107721c7c36ec61da54337a4b7d31d23df6249315f7b6c6e8159b
MD5 9989c63a9b75688448e178394e96a935
BLAKE2b-256 8f2d83de6d5fde48242d26c369a034522776f9106c0b2e5db7cd4e562cd000fa

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page