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122 lines
7.1 KiB
Markdown
122 lines
7.1 KiB
Markdown
[](https://badge.fury.io/py/FLAML)
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[](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
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[](https://pepy.tech/project/flaml)
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[](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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# A Fast Library for Automated Machine Learning & Tuning
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<p align="center">
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<img src="https://github.com/microsoft/FLAML/blob/main/website/static/img/flaml.svg" width=200>
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<br>
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</p>
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:fire: **Update (2022/08): We will give a [hands-on tutorial on FLAML at KDD 2022](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) on 08/16/2022.**
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## What is FLAML
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FLAML is a lightweight Python library that finds accurate machine
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learning models automatically, efficiently and economically. It frees users from selecting
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learners and hyperparameters for each learner. It can also be used to tune generic hyperparameters for MLOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations and so on.
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1. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classifcal machine learning models and deep neural networks.
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1. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training and evaluation code).
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1. It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, [cost-effective
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hyperparameter optimization](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function/#hyperparameter-optimization-algorithm)
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and learner selection method invented by Microsoft Research.
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FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET. In ML.NET, you can use FLAML via low-code solutions like [Model Builder](https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder) Visual Studio extension and the cross-platform [ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/automate-training-with-cli). Alternatively, you can use the [ML.NET AutoML API](https://www.nuget.org/packages/Microsoft.ML.AutoML/#versions-body-tab) for a code-first experience.
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## Installation
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### Python
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FLAML requires **Python version >= 3.7**. It can be installed from pip:
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```bash
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pip install flaml
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```
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To run the [`notebook examples`](https://github.com/microsoft/FLAML/tree/main/notebook),
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install flaml with the [notebook] option:
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```bash
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pip install flaml[notebook]
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```
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### .NET
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Use the following guides to get started with FLAML in .NET:
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- [Install Model Builder](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-model-builder?tabs=visual-studio-2022)
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- [Install ML.NET CLI](https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-ml-net-cli?tabs=windows)
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- [Microsoft.AutoML](https://www.nuget.org/packages/Microsoft.ML.AutoML/0.20.0-preview.22313.1)
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## Quickstart
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* With three lines of code, you can start using this economical and fast
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AutoML engine as a [scikit-learn style estimator](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML).
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```python
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from flaml import AutoML
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automl = AutoML()
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automl.fit(X_train, y_train, task="classification")
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```
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* You can restrict the learners and use FLAML as a fast hyperparameter tuning
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tool for XGBoost, LightGBM, Random Forest etc. or a [customized learner](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#estimator-and-search-space).
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```python
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automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
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```
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* You can also run generic hyperparameter tuning for a [custom function](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
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```python
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from flaml import tune
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tune.run(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)
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```
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* [Zero-shot AutoML](https://microsoft.github.io/FLAML/docs/Use-Cases/Zero-Shot-AutoML) allows using the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task.
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```python
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from flaml.default import LGBMRegressor
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# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
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estimator = LGBMRegressor()
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# The hyperparameters are automatically set according to the training data.
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estimator.fit(X_train, y_train)
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```
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## Documentation
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You can find a detailed documentation about FLAML [here](https://microsoft.github.io/FLAML/) where you can find the API documentation, use cases and examples.
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In addition, you can find:
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- [Talks](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A) and [tutorials](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) about FLAML.
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- Research around FLAML [here](https://microsoft.github.io/FLAML/docs/Research).
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- FAQ [here](https://microsoft.github.io/FLAML/docs/FAQ).
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- Contributing guide [here](https://microsoft.github.io/FLAML/docs/Contribute).
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- ML.NET documentation and tutorials for [Model Builder](https://docs.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder), [ML.NET CLI](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis-cli), and [AutoML API](https://github.com/dotnet/csharp-notebooks/blob/main/machine-learning/03-Training%20and%20AutoML.ipynb).
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## Contributing
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This project welcomes contributions and suggestions. Most contributions require you to agree to a
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
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the rights to use your contribution. For details, visit <https://cla.opensource.microsoft.com>.
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If you are new to GitHub [here](https://help.github.com/categories/collaborating-with-issues-and-pull-requests/) is a detailed help source on getting involved with development on GitHub.
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When you submit a pull request, a CLA bot will automatically determine whether you need to provide
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a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
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provided by the bot. You will only need to do this once across all repos using our CLA.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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