Metadata-Version: 2.1
Name: FLAML
Version: 0.5.2
Summary: A fast and lightweight autoML system
Home-page: https://github.com/microsoft/FLAML
Author: Microsoft Corporation
Author-email: hpo@microsoft.com
License: UNKNOWN
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        # FLAML - Fast and Lightweight AutoML
        
        <p align="center">
            <img src="https://github.com/microsoft/FLAML/blob/main/docs/images/FLAML.png"  width=200>
            <br>
        </p>
        
        FLAML is a lightweight Python library that finds accurate machine
        learning models automatically, efficiently and economically. It frees users from selecting
        learners and hyperparameters for each learner. It is fast and economical. 
        The simple and lightweight design makes it easy to extend, such as
        adding customized learners or metrics. FLAML is powered by a new, [cost-effective
        hyperparameter optimization](https://github.com/microsoft/FLAML/tree/main/flaml/tune)
        and learner selection method invented by Microsoft Research.
        FLAML leverages the structure of the search space to choose a search order optimized for both cost and error. For example, the system tends to propose cheap configurations at the beginning stage of the search,
        but quickly moves to configurations with high model complexity and large sample size when needed in the later stage of the search. For another example, it favors cheap learners in the beginning but penalizes them later if the error improvement is slow. The cost-bounded search and cost-based prioritization make a big difference in the search efficiency under budget constraints.
        
        ## Installation
        
        FLAML requires **Python version >= 3.6**. It can be installed from pip:
        
        ```bash
        pip install flaml
        ```
        
        To run the [`notebook example`](https://github.com/microsoft/FLAML/tree/main/notebook),
        install flaml with the [notebook] option:
        
        ```bash
        pip install flaml[notebook]
        ```
        
        ## Quickstart
        
        * With three lines of code, you can start using this economical and fast
        AutoML engine as a scikit-learn style estimator.
        ```python
        from flaml import AutoML
        automl = AutoML()
        automl.fit(X_train, y_train, task="classification")
        ```
        
        * You can restrict the learners and use FLAML as a fast hyperparameter tuning
        tool for XGBoost, LightGBM, Random Forest etc. or a customized learner.
        ```python
        automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
        ```
        
        * You can also run generic ray-tune style hyperparameter tuning for a custom function.
        ```python
        from flaml import tune
        tune.run(train_with_config, config={â€¦}, low_cost_partial_config={â€¦}, time_budget_s=3600)
        ```
        
        ## Advantages
        
        * For classification and regression tasks, find quality models with lower computational resources.
        * Users can choose their desired customizability: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), full customization (arbitrary training and evaluation code).
        * Allow human guidance in hyperparameter tuning to respect prior on certain subspaces but also able to explore other subspaces. Read more about the 
        hyperparameter optimization methods
        in FLAML [here](https://github.com/microsoft/FLAML/tree/main/flaml/tune). They can be used beyond the AutoML context. 
        And they can be used in distributed HPO frameworks such as ray tune or nni.
        * Support online AutoML: automatic hyperparameter tuning for online learning algorithms. Read more about the online AutoML method in FLAML [here](https://github.com/microsoft/FLAML/tree/main/flaml/onlineml).
        
        ## Examples
        
        A basic classification example.
        
        ```python
        from flaml import AutoML
        from sklearn.datasets import load_iris
        # Initialize an AutoML instance
        automl = AutoML()
        # Specify automl goal and constraint
        automl_settings = {
            "time_budget": 10,  # in seconds
            "metric": 'accuracy',
            "task": 'classification',
            "log_file_name": "test/iris.log",
        }
        X_train, y_train = load_iris(return_X_y=True)
        # Train with labeled input data
        automl.fit(X_train=X_train, y_train=y_train,
                                **automl_settings)
        # Predict
        print(automl.predict_proba(X_train))
        # Export the best model
        print(automl.model)
        ```
        
        A basic regression example.
        
        ```python
        from flaml import AutoML
        from sklearn.datasets import load_boston
        # Initialize an AutoML instance
        automl = AutoML()
        # Specify automl goal and constraint
        automl_settings = {
            "time_budget": 10,  # in seconds
            "metric": 'r2',
            "task": 'regression',
            "log_file_name": "test/boston.log",
        }
        X_train, y_train = load_boston(return_X_y=True)
        # Train with labeled input data
        automl.fit(X_train=X_train, y_train=y_train,
                                **automl_settings)
        # Predict
        print(automl.predict(X_train))
        # Export the best model
        print(automl.model)
        ```
        
        More examples can be found in [notebooks](https://github.com/microsoft/FLAML/tree/main/notebook/).
        
        ## Documentation
        
        Please find the API documentation [here](https://microsoft.github.io/FLAML/).
        
        Please find demo and tutorials of FLAML [here](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A)
        
        
        For more technical details, please check our papers.
        
        * [FLAML: A Fast and Lightweight AutoML Library](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/). Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys, 2021.
        ```
        @inproceedings{wang2021flaml,
            title={FLAML: A Fast and Lightweight AutoML Library},
            author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},
            year={2021},
            booktitle={MLSys},
        }
        ```
        * [Frugal Optimization for Cost-related Hyperparameters](https://arxiv.org/abs/2005.01571). Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.
        * [Economical Hyperparameter Optimization With Blended Search Strategy](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/). Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021.
        * ChaCha for online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. To appear in ICML 2021.
        
        ## Contributing
        
        This project welcomes contributions and suggestions. Most contributions require you to agree to a
        Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
        the rights to use your contribution. For details, visit <https://cla.opensource.microsoft.com>.
        
        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.
        
        When you submit a pull request, a CLA bot will automatically determine whether you need to provide
        a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
        provided by the bot. You will only need to do this once across all repos using our CLA.
        
        This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
        For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
        contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
        
        ## Developing
        
        ### Setup:
        
        ```
        git clone https://github.com/microsoft/FLAML.git
        pip install -e .[test,notebook]
        ```
        
        ### Coverage
        Any code you commit should generally not significantly impact coverage. To run all unit tests:
        ```
        coverage run -m pytest test
        ```
        
        If all the tests are passed, please also test run notebook/flaml_automl to make sure your commit does not break the notebook example.
        
        ## Authors
        
        * Chi Wang
        * Qingyun Wu
        
        Contributors (alphabetical order): Sebastien Bubeck, Surajit Chaudhuri, Nadiia Chepurko, Ofer Dekel, Alex Deng, Anshuman Dutt, Nicolo Fusi, Jianfeng Gao, Johannes Gehrke, Silu Huang, Dongwoo Kim, Christian Konig, John Langford, Amin Saied, Neil Tenenholtz, Markus Weimer, Haozhe Zhang, Erkang Zhu.
        
        ## License
        
        [MIT License](LICENSE)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: notebook
Provides-Extra: test
Provides-Extra: blendsearch
Provides-Extra: ray
Provides-Extra: azureml
Provides-Extra: nni
Provides-Extra: vw
