Metadata-Version: 2.1
Name: FLAML
Version: 0.1.2
Summary: A fast and lightweight autoML system
Home-page: https://github.com/microsoft/FLAML
Author: Microsoft Corporation
Author-email: hpo@microsoft.com
License: UNKNOWN
Description: # FLAML - Fast and Lightweight AutoML
        
        FLAML is a Python library designed to automatically produce accurate machine
        learning models with low computational cost. It frees users from selecting
        learners and hyperparameters for each learner. It is fast and cheap.
        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 and learner selection method invented by
        Microsoft Research.
        FLAML is easy to use:
        
        * 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 embed FLAML in self-tuning software for just-in-time tuning with
        low latency & resource consumption.
        ```python
        automl.fit(X_train, y_train, task="regression", time_budget=60)
        ```
        
        ## 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]
        ```
        
        ## Examples
        
        A basic classification example.
        
        ```python
        from flaml import AutoML
        from sklearn.datasets import load_iris
        # Initialize the FLAML learner.
        automl = AutoML()
        # Provide configurations.
        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 the FLAML learner.
        automl = AutoML()
        # Provide configurations.
        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: see the [notebook](https://github.com/microsoft/FLAML/tree/main/notebook/flaml_demo.ipynb)
        
        ## 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>.
        
        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.
        
        ## Authors
        
        * Chi Wang
        * Qingyun Wu
        * Erkang Zhu
        
        Contributors: Markus Weimer, Silu Huang, Haozhe Zhang, Alex Deng.
        
        ## 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
