autogluon.features

Pre-release

This release is a pre-release and may not be stable for production use.

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

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==0.6.2

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
TextPredictor
ImagePredictor
ObjectDetector
MultiModalPredictor
TimeSeriesPredictor

Resources

See the AutoGluon Website for documentation and instructions on:

Installing AutoGluon
Learning with tabular data
- Tips to maximize accuracy (if benchmarking, make sure to run fit() with argument presets='best_quality').
Learning with text data
Learning with image data
Learning with time series data

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

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}
}

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.

Metadata

Release files for autogluon.features 0.6.3b20230114

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for autogluon.features 0.6.3b20230114
File	Size	Uploaded
autogluon.features-0.6.3b20230114.tar.gz	43.7 kB	Jan 14, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for autogluon.features 0.6.3b20230114
File	Interpreter	ABI	Platform	Reset
autogluon.features-0.6.3b20230114-py3-none-any.whl 60.2 kB Jan 14, 2023	Python 3	none	any	Details

Total release size: 103.9 kB

Release files / autogluon.features-0.6.3b20230114.tar.gz

Download URL	autogluon.features-0.6.3b20230114.tar.gz
Size	43.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`64df88df99e799fed647c514c505a8446371fd066df36396ce8a2e7c0cde5148`
BLAKE2b-256 checksum How to use checksums	`8099d228a839e2c1964978ff9021383e716951e29688717266672274dbc46975`
Upload date	Jan 14, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.7.15`

Release files / autogluon.features-0.6.3b20230114-py3-none-any.whl

Download URL	autogluon.features-0.6.3b20230114-py3-none-any.whl
Size	60.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`85590cf4dc3ca86a45c936754d60126be6756ebc7ebd449ac19187e3df9c7773`
BLAKE2b-256 checksum How to use checksums	`49f83b2281a5c3bb33b12a120d7e97eac410dd331351f7e10663e8be4b8104e1`
Upload date	Jan 14, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.7.15`