Metadata-Version: 2.1
Name: autogluon.features
Version: 1.1.2b20241114
Summary: Fast and Accurate ML in 3 Lines of Code
Home-page: https://github.com/autogluon/autogluon
Author: AutoGluon Community
License: Apache-2.0
Project-URL: Documentation, https://auto.gluon.ai
Project-URL: Bug Reports, https://github.com/autogluon/autogluon/issues
Project-URL: Source, https://github.com/autogluon/autogluon/
Project-URL: Contribute!, https://github.com/autogluon/autogluon/blob/master/CONTRIBUTING.md
Description: 
        
        <div align="center">
        <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
        
        ## Fast and Accurate ML in 3 Lines of Code
        
        [![Latest Release](https://img.shields.io/github/v/release/autogluon/autogluon)](https://github.com/autogluon/autogluon/releases)
        [![Conda Forge](https://img.shields.io/conda/vn/conda-forge/autogluon.svg)](https://anaconda.org/conda-forge/autogluon)
        [![Python Versions](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)](https://pypi.org/project/autogluon/)
        [![Downloads](https://pepy.tech/badge/autogluon/month)](https://pepy.tech/project/autogluon)
        [![GitHub license](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](./LICENSE)
        [![Discord](https://img.shields.io/discord/1043248669505368144?logo=discord&style=flat)](https://discord.gg/wjUmjqAc2N)
        [![Twitter](https://img.shields.io/twitter/follow/autogluon?style=social)](https://twitter.com/autogluon)
        [![Continuous Integration](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml)
        [![Platform Tests](https://github.com/autogluon/autogluon/actions/workflows/platform_tests-command.yml/badge.svg?event=schedule)](https://github.com/autogluon/autogluon/actions/workflows/platform_tests-command.yml)
        
        [Installation](https://auto.gluon.ai/stable/install.html) | [Documentation](https://auto.gluon.ai/stable/index.html) | [Release Notes](https://auto.gluon.ai/stable/whats_new/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.
        </div>
        
        ## 💾 Installation
        
        AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.
        
        You can install AutoGluon with:
        
        ```python
        pip install autogluon
        ```
        
        Visit our [Installation Guide](https://auto.gluon.ai/stable/install.html) for detailed instructions, including GPU support, Conda installs, and optional dependencies.
        
        ## :zap: Quickstart
        
        Build accurate end-to-end ML models in just 3 lines of code!
        
        ```python
        from autogluon.tabular import TabularPredictor
        predictor = TabularPredictor(label="class").fit("train.csv")
        predictions = predictor.predict("test.csv")
        ```
        
        | 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/tabular-quick-start.html) |                 [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html)                 |
        | MultiModalPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/multimodal/multimodal_prediction/multimodal-quick-start.html)            | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.multimodal.MultiModalPredictor.html) |
        | TimeSeriesPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quick-start.html)            | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.html) |
        
        ## :mag: Resources
        
        ### Hands-on Tutorials / Talks
        
        Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available [here](AWESOME.md#videos--tutorials).
        
        | Title                                                                                                                    | Format   | Location                                                                         | Date       |
        |--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------|
        | :tv: [AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https://www.youtube.com/watch?v=5tvp_Ihgnuk) | Tutorial | [AutoML Conf 2023](https://2023.automl.cc/)                                      | 2023/09/12 |
        | :sound: [AutoGluon: The Story](https://automlpodcast.com/episode/autogluon-the-story)                                    | Podcast  | [The AutoML Podcast](https://automlpodcast.com/)                                 | 2023/09/05 |
        | :tv: [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https://youtu.be/Lwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin                                                                    | 2023/06/20 | 
        | :tv: [Solving Complex ML Problems in a few Lines of Code with AutoGluon](https://www.youtube.com/watch?v=J1UQUCPB88I)    | Tutorial | PyData Seattle                                                                   | 2023/06/20 | 
        | :tv: [The AutoML Revolution](https://www.youtube.com/watch?v=VAAITEds-28)                                                | Tutorial | [Fall AutoML School 2022](https://sites.google.com/view/automl-fall-school-2022) | 2022/10/18 |
        
        ### Scientific Publications
        - [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))
        - [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) (*NeurIPS*, 2020) ([BibTeX](CITING.md#tabular-distillation))
        - [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper-round2.pdf) (*NeurIPS*, 2021) ([BibTeX](CITING.md#autogluonmultimodal))
        - [XTab: Cross-table Pretraining for Tabular Transformers](https://proceedings.mlr.press/v202/zhu23k/zhu23k.pdf) (*ICML*, 2023)
        - [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2308.05566) (*AutoML Conf*, 2023) ([BibTeX](CITING.md#autogluontimeseries))
        - [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https://arxiv.org/pdf/2311.02971.pdf) (*Under Review*, 2024)
        
        ### Articles
        - [AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library](https://towardsdatascience.com/autogluon-timeseries-every-time-series-forecasting-model-in-one-library-29a3bf6879db) (*Towards Data Science*, Jan 2024)
        - [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)
        - [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)
        
        ### Train/Deploy AutoGluon in the Cloud
        - [AutoGluon Cloud](https://auto.gluon.ai/cloud/stable/index.html) (Recommended)
        - [AutoGluon on SageMaker AutoPilot](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/autopilot-autogluon.html)
        - [AutoGluon on Amazon SageMaker](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/cloud-aws-sagemaker-train-deploy.html)
        - [AutoGluon Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#autogluon-training-containers) (Security certified & maintained by the AutoGluon developers)
        - [AutoGluon Official Docker Container](https://hub.docker.com/r/autogluon/autogluon)
        - [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism) (Not maintained by us)
        
        ## :pencil: Citing AutoGluon
        
        If you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).
        
        ## :wave: How to get involved
        
        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.
        
        ## :classical_building: License
        
        This library is licensed under the Apache 2.0 License.
        
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 :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.9, <3.13
Description-Content-Type: text/markdown
