Metadata-Version: 2.1
Name: autogluon.text
Version: 0.3.2b20211226
Summary: AutoML for Text, Image, and Tabular Data
Home-page: https://github.com/awslabs/autogluon
Author: AutoGluon Community
License: Apache-2.0
Project-URL: Documentation, https://auto.gluon.ai
Project-URL: Bug Reports, https://github.com/awslabs/autogluon/issues
Project-URL: Source, https://github.com/awslabs/autogluon/
Project-URL: Contribute!, https://github.com/awslabs/autogluon/blob/master/CONTRIBUTING.md
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Customer Service
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Telecommunications Industry
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.7, <3.10
Description-Content-Type: text/markdown
Requires-Dist: numpy (<1.22,>=1.19)
Requires-Dist: scipy (<1.7,>=1.5.4)
Requires-Dist: pandas (<2.0,>=1.0.0)
Requires-Dist: scikit-learn (<1.1,>=1.0.0)
Requires-Dist: tqdm (>=4.38.0)
Requires-Dist: autogluon.core (==0.3.2b20211226)
Requires-Dist: autogluon-contrib-nlp (==0.0.1b20210201)
Requires-Dist: pytest



<div align="left">
  <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
</div>

## AutoML for Text, Image, and Tabular Data

[![Build Status](https://ci.gluon.ai/view/all/job/autogluon/job/master/badge/icon)](https://ci.gluon.ai/view/all/job/autogluon/job/master/)
[![Pypi Version](https://img.shields.io/pypi/v/autogluon.svg)](https://pypi.org/project/autogluon/#history)
[![GitHub license](docs/static/apache2.svg)](./LICENSE)
[![Downloads](https://pepy.tech/badge/autogluon)](https://pepy.tech/project/autogluon)
![Upload Python Package](https://github.com/awslabs/autogluon/workflows/Upload%20Python%20Package/badge.svg)

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

```python
# First install package from terminal:
# python3 -m pip install -U pip
# python3 -m pip install -U setuptools wheel
# python3 -m pip install autogluon  # autogluon==0.3.1

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](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-0) |
| TextPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/text_prediction/beginner.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-3) |
| ImagePredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/image_prediction/beginner.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-1) |
| ObjectDetector | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/object_detection/beginner.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-2) |

## Resources

See the [AutoGluon Website](https://auto.gluon.ai/stable/index.html) for [documentation](https://auto.gluon.ai/stable/api/index.html) and instructions on:
- [Installing AutoGluon](https://auto.gluon.ai/stable/index.html#installation)
- [Learning with tabular data](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html)
  - [Tips to maximize accuracy](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html#maximizing-predictive-performance) (if **benchmarking**, make sure to run `fit()` with argument `presets='best_quality'`).  

- [Learning with text data](https://auto.gluon.ai/stable/tutorials/text_prediction/beginner.html)
- [Learning with image data](https://auto.gluon.ai/stable/tutorials/image_prediction/beginner.html)
- More advanced topics such as [Neural Architecture Search](https://auto.gluon.ai/stable/tutorials/nas/index.html)

### Scientific Publications
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020)
- [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) (*NeurIPS*, 2020)
- [Multimodal AutoML on Structured Tables with Text Fields](https://openreview.net/pdf?id=OHAIVOOl7Vl) (*ICML AutoML Workshop*, 2021)

### Articles
- [AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions](https://aws.amazon.com/blogs/opensource/machine-learning-with-autogluon-an-open-source-automl-library/) (*AWS Open Source Blog*, Mar 2020)
- [Accurate image classification in 3 lines of code with AutoGluon](https://medium.com/@zhanghang0704/image-classification-on-kaggle-using-autogluon-fc896e74d7e8) (*Medium*, Feb 2020)
- [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)

### Hands-on Tutorials
- [Practical Automated Machine Learning with Tabular, Text, and Image Data (KDD 2020)](https://jwmueller.github.io/KDD20-tutorial/)

### Train/Deploy AutoGluon in the Cloud
- [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism)
- [Running AutoGluon-Tabular on Amazon SageMaker](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb)
- [Running AutoGluon Image Classification on Amazon SageMaker](https://github.com/zhanghang1989/AutoGluon-Docker)

## 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."](https://arxiv.org/abs/2003.06505) arXiv preprint arXiv:2003.06505 (2020).

BibTeX entry:

```bibtex
@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."](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) Advances in Neural Information Processing Systems 33 (2020).

BibTeX entry:

```bibtex
@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."](https://openreview.net/forum?id=OHAIVOOl7Vl) 8th ICML Workshop on Automated Machine Learning (AutoML). 2021.

BibTeX entry:

```bibtex
@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 also provides state-of-the-art tools for hyperparameter optimization, such as for example ASHA, Hyperband, Bayesian Optimization and BOHB.

To get started, checkout our paper ["Model-based Asynchronous Hyperparameter and Neural Architecture Search"](https://arxiv.org/abs/2003.10865) arXiv preprint arXiv:2003.10865 (2020).

```bibtex
@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.

## 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](https://github.com/awslabs/autogluon/blob/master/CONTRIBUTING.md) to get started.


