<div align="left">
<img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
</div>
## AutoML for Image, Text, Time Series, and Tabular Data
[](https://github.com/autogluon/autogluon/releases)
[](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml)
[](https://github.com/autogluon/autogluon/actions/workflows/platform_tests-command.yml)
[](https://pypi.org/project/autogluon/)
[](./LICENSE)
[](https://pepy.tech/project/autogluon)
[](https://twitter.com/autogluon)
[Install Instructions](https://auto.gluon.ai/stable/install.html) | Documentation ([Stable](https://auto.gluon.ai/stable/index.html) | [Latest](https://auto.gluon.ai/dev/index.html))
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
```python
# First install package from terminal:
# pip install -U pip
# pip install -U setuptools wheel
# pip install autogluon # autogluon==0.7.0
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 | [](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html) | [](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-0) |
| MultiModalPredictor | [](https://auto.gluon.ai/stable/tutorials/multimodal/index.html) | [](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.multimodal.MultiModalPredictor) |
| TimeSeriesPredictor | [](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quickstart.html) | [](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.timeseries.TimeSeriesPredictor) |
## 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 multimodal data (image, text, etc.)](https://auto.gluon.ai/stable/tutorials/multimodal/index.html)
- [Learning with time series data](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quickstart.html)
Refer to the [AutoGluon Roadmap](https://github.com/autogluon/autogluon/blob/master/ROADMAP.md) for details on upcoming features and releases.
### 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)
- [AutoGluon-Tabular on Amazon SageMaker](https://github.com/aws/amazon-sagemaker-examples/tree/master/advanced_functionality/autogluon-tabular-containers)
- [AutoGluon Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#autogluon-training-containers)
## 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/autogluon/autogluon/blob/master/CONTRIBUTING.md) 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."](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'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](https://github.com/awslabs/syne-tune).
To learn more, 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.
Raw data
{
"_id": null,
"home_page": "https://github.com/autogluon/autogluon",
"name": "aglite-test.tabular",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8, <3.11",
"maintainer_email": "",
"keywords": "",
"author": "AutoGluon Community",
"author_email": "",
"download_url": "",
"platform": null,
"description": "\n\n\n<div align=\"left\">\n <img src=\"https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png\" width=\"350\">\n</div>\n\n## AutoML for Image, Text, Time Series, and Tabular Data\n\n[](https://github.com/autogluon/autogluon/releases)\n[](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml)\n[](https://github.com/autogluon/autogluon/actions/workflows/platform_tests-command.yml)\n[](https://pypi.org/project/autogluon/)\n[](./LICENSE)\n[](https://pepy.tech/project/autogluon)\n[](https://twitter.com/autogluon)\n\n[Install Instructions](https://auto.gluon.ai/stable/install.html) | Documentation ([Stable](https://auto.gluon.ai/stable/index.html) | [Latest](https://auto.gluon.ai/dev/index.html))\n\nAutoGluon 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.\n\n## Example\n\n```python\n# First install package from terminal:\n# pip install -U pip\n# pip install -U setuptools wheel\n# pip install autogluon # autogluon==0.7.0\n\nfrom autogluon.tabular import TabularDataset, TabularPredictor\ntrain_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')\ntest_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')\npredictor = TabularPredictor(label='class').fit(train_data, time_limit=120) # Fit models for 120s\nleaderboard = predictor.leaderboard(test_data)\n```\n\n| AutoGluon Task | Quickstart | API |\n|:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n| TabularPredictor | [](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html) | [](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-0) |\n| MultiModalPredictor | [](https://auto.gluon.ai/stable/tutorials/multimodal/index.html) | [](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.multimodal.MultiModalPredictor) |\n| TimeSeriesPredictor | [](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quickstart.html) | [](https://auto.gluon.ai/stable/api/autogluon.predictor.html#autogluon.timeseries.TimeSeriesPredictor) |\n\n## Resources\n\nSee the [AutoGluon Website](https://auto.gluon.ai/stable/index.html) for [documentation](https://auto.gluon.ai/stable/api/index.html) and instructions on:\n- [Installing AutoGluon](https://auto.gluon.ai/stable/index.html#installation)\n- [Learning with tabular data](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html)\n - [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'`). \n\n- [Learning with multimodal data (image, text, etc.)](https://auto.gluon.ai/stable/tutorials/multimodal/index.html)\n- [Learning with time series data](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quickstart.html)\n\nRefer to the [AutoGluon Roadmap](https://github.com/autogluon/autogluon/blob/master/ROADMAP.md) for details on upcoming features and releases.\n\n### Scientific Publications\n- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020)\n- [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) (*NeurIPS*, 2020)\n- [Multimodal AutoML on Structured Tables with Text Fields](https://openreview.net/pdf?id=OHAIVOOl7Vl) (*ICML AutoML Workshop*, 2021)\n\n### Articles\n- [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)\n- [Accurate image classification in 3 lines of code with AutoGluon](https://medium.com/@zhanghang0704/image-classification-on-kaggle-using-autogluon-fc896e74d7e8) (*Medium*, Feb 2020)\n- [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)\n\n### Hands-on Tutorials\n- [Practical Automated Machine Learning with Tabular, Text, and Image Data (KDD 2020)](https://jwmueller.github.io/KDD20-tutorial/)\n\n### Train/Deploy AutoGluon in the Cloud\n- [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism)\n- [AutoGluon-Tabular on Amazon SageMaker](https://github.com/aws/amazon-sagemaker-examples/tree/master/advanced_functionality/autogluon-tabular-containers)\n- [AutoGluon Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#autogluon-training-containers)\n\n## Contributing to AutoGluon\n\nWe 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/autogluon/autogluon/blob/master/CONTRIBUTING.md) to get started.\n\n## Citing AutoGluon\n\nIf you use AutoGluon in a scientific publication, please cite the following paper:\n\nErickson, Nick, et al. [\"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.\"](https://arxiv.org/abs/2003.06505) arXiv preprint arXiv:2003.06505 (2020).\n\nBibTeX entry:\n\n```bibtex\n@article{agtabular,\n title={AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data},\n author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander},\n journal={arXiv preprint arXiv:2003.06505},\n year={2020}\n}\n```\n\nIf you are using AutoGluon Tabular's model distillation functionality, please cite the following paper:\n\nFakoor, 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).\n\nBibTeX entry:\n\n```bibtex\n@article{agtabulardistill,\n title={Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation},\n author={Fakoor, Rasool and Mueller, Jonas W and Erickson, Nick and Chaudhari, Pratik and Smola, Alexander J},\n journal={Advances in Neural Information Processing Systems},\n volume={33},\n year={2020}\n}\n```\n\nIf you use AutoGluon's multimodal text+tabular functionality in a scientific publication, please cite the following paper:\n\nShi, 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.\n\nBibTeX entry:\n\n```bibtex\n@inproceedings{agmultimodaltext,\n title={Multimodal AutoML on Structured Tables with Text Fields},\n author={Shi, Xingjian and Mueller, Jonas and Erickson, Nick and Li, Mu and Smola, Alex},\n booktitle={8th ICML Workshop on Automated Machine Learning (AutoML)},\n year={2021}\n}\n```\n\n\n## AutoGluon for Hyperparameter Optimization\n\nAutoGluon'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](https://github.com/awslabs/syne-tune).\n\nTo learn more, checkout our paper [\"Model-based Asynchronous Hyperparameter and Neural Architecture Search\"](https://arxiv.org/abs/2003.10865) arXiv preprint arXiv:2003.10865 (2020).\n\n```bibtex\n@article{abohb,\n title={Model-based Asynchronous Hyperparameter and Neural Architecture Search},\n author={Klein, Aaron and Tiao, Louis and Lienart, Thibaut and Archambeau, Cedric and Seeger, Matthias},\n journal={arXiv preprint arXiv:2003.10865},\n year={2020}\n}\n```\n\n\n## License\n\nThis library is licensed under the Apache 2.0 License.\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "AutoML for Image, Text, and Tabular Data",
"version": "0.7.0b20230314",
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "d36421bd9f37771894d658284bd05b0ebfee455bace775594d1b4d6c04a2371d",
"md5": "db03814695546a0cc878f3dda0599929",
"sha256": "9cbf569161ae886e241ffd2f0d13cf82b258a55d6fb0f67bc77f9074879083da"
},
"downloads": -1,
"filename": "aglite_test.tabular-0.7.0b20230314-py3-none-any.whl",
"has_sig": false,
"md5_digest": "db03814695546a0cc878f3dda0599929",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8, <3.11",
"size": 288358,
"upload_time": "2023-03-14T22:19:22",
"upload_time_iso_8601": "2023-03-14T22:19:22.329516Z",
"url": "https://files.pythonhosted.org/packages/d3/64/21bd9f37771894d658284bd05b0ebfee455bace775594d1b4d6c04a2371d/aglite_test.tabular-0.7.0b20230314-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-03-14 22:19:22",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "autogluon",
"github_project": "autogluon",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "aglite-test.tabular"
}
JSON
