Metadata-Version: 2.1
Name: openbox
Version: 0.7.7
Summary: Efficient and generalized blackbox optimization (BBO) system
Home-page: https://github.com/thomas-young-2013/open-box
Author: Thomas (Yang) Li from DIR Lab@PKU
License: MIT
Description: <p align="center">
        <img src="https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/logos/logo.png" width="68%">
        </p>
        
        -----------
        
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        ## OpenBox: Generalized and Efficient Blackbox Optimization System.
        OpenBox is an efficient and generalized blackbox optimization (BBO) system, which owns the following characteristics:
        1. Basic BBO algorithms.
        2. BBO with constraints.
        3. BBO with multiple objectives.
        4. BBO with transfer learning.
        5. BBO with distributed parallelization.
        6. BBO with multi-fidelity acceleration.
        7. BBO with early stops.
        
        
        ## Deployment Artifacts
        #### Standalone Python package.
        Users can install the released package and use it using Python.
        
        #### Distributed BBO service.
        We adopt the "BBO as a service" paradigm and implement OpenBox as a managed general service for black-box optimization. Users can access this service via REST API conveniently, and do not need to worry about other issues such as environment setup, software maintenance, programming, and optimization of the execution. Moreover, we also provide a Web UI,
        through which users can easily track and manage the tasks.
        
        
        ## Features
        
        + Ease of use. Minimal user configuration and setup, and necessary visualization for optimization process. 
        + Performance standards. Host state-of-the-art optimization algorithms; select proper algorithms automatically.
        + Cost-oriented management. Give cost-model based suggestions to users, e.g., minimal machines or time-budget. 
        + Scalability. Scale to dimensions on the number of input variables, objectives, tasks, trials, and parallel evaluations.
        + High efficiency. Effective use of parallel resource, speeding up optimization with transfer-learning, and multi-fidelity acceleration for computationally-expensive evaluations. 
        + Data privacy protection, robustness and extensibility.
        
        ## Links
        + Blog post: [to appear soon]()
        + Documentation: https://open-box.readthedocs.io/en/latest/?badge=latest
        + Pypi package: https://pypi.org/project/open-box/
        + Conda package: [to appear soon]()
        + Examples: https://github.com/thomas-young-2013/open-box/tree/master/examples
        
        ## Benchmark Results
        
        Single-objective problems
        Ackley-4                  | Hartmann
        :-------------------------:|:-------------------------:
        ![](https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/experiments/so_math_ackley-4.png)  |  ![](https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/experiments/so_math_hartmann.png)
        
        Single-objective problems with constraints
        Mishra                  | Keane-10
        :-------------------------:|:-------------------------:
        ![](https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/experiments/soc_math_mishra.png)  |  ![](https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/experiments/soc_math_keane.png)
        
        Multi-objective problems
        
        DTLZ1-6-5             | ZDT2-3 
        :-------------------------:|:-------------------------:
        ![](https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/experiments/mo_math_dtlz1-6-5.png)  |  ![](https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/experiments/mo_math_zdt2-3.png)
        
        Multi-objective problems with constraints
        
        CONSTR             | SRN 
        :-------------------------:|:-------------------------:
        ![](https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/experiments/moc_math_constr.png)  |  ![](https://raw.githubusercontent.com/thomas-young-2013/open-box/master/docs/experiments/moc_math_srn.png)
        
        ## Installation
        
        **Installation via pip**
        
        For Windows and Linux users, you can install by
        
        ```bash
        pip install open-box
        ```
        
        For macOS users, you need to install `pyrfr` correctly first, and then `pip install open-box`. 
        
        The tips for installing `pyrfr` on macOS is [here](docs/source/installation/install-pyrfr-on-macos.md).
        
        **Manual installation from the github source**
        
         ```bash
        git clone https://github.com/thomas-young-2013/open-box.git && cd open-box
        cat requirements.txt | xargs -n 1 -L 1 pip install
        python setup.py install
         ```
        macOS users still need to follow the [tips](docs/source/installation/install-pyrfr-on-macos.md) 
        to install `pyrfr` correctly first.
        
        ## Quick Start
        
        ```python
        import numpy as np
        from openbox.utils.start_smbo import create_smbo
        
        
        def branin(x):
            xs = x.get_dictionary()
            x1 = xs['x1']
            x2 = xs['x2']
            a = 1.
            b = 5.1 / (4. * np.pi ** 2)
            c = 5. / np.pi
            r = 6.
            s = 10.
            t = 1. / (8. * np.pi)
            ret = a * (x2 - b * x1 ** 2 + c * x1 - r) ** 2 + s * (1 - t) * np.cos(x1) + s
            return {'objs': (ret,)}
        
        
        config_dict = {
            "optimizer": "SMBO",
            "parameters": {
                "x1": {
                    "type": "float",
                    "bound": [-5, 10],
                    "default": 0
                },
                "x2": {
                    "type": "float",
                    "bound": [0, 15]
                },
            },
            "advisor_type": 'default',
            "max_runs": 90,
            "time_limit_per_trial": 5,
            "logging_dir": 'logs',
            "task_id": 'hp1'
        }
        
        bo = create_smbo(branin, **config_dict)
        bo.run()
        inc_value = bo.get_incumbent()
        print('BO', '=' * 30)
        print(inc_value)
        ```
        
        ## **Releases and Contributing**
        OpenBox has a frequent release cycle. Please let us know if you encounter a bug by [filling an issue](https://github.com/thomas-young-2013/open-box/issues/new/choose).
        
        We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.
        
        If you plan to contribute new features, new modules, etc. please first open an issue or reuse an existing issue, and discuss the feature with us.
        
        To learn more about making a contribution to OpenBox, please refer to our [How-to contribution page](https://github.com/thomas-young-2013/open-box/blob/master/CONTRIBUTING.md). 
        
        We appreciate all contributions and thank all the contributors!
        
        
        ## **Feedback**
        * [File an issue](https://github.com/thomas-young-2013/open-box/issues) on GitHub.
        * Email us via *liyang.cs@pku.edu.cn*.
        
        
        ## Related Projects
        
        Targeting at openness and advancing AutoML ecosystems, we had also released few other open source projects.
        
        * [VocalnoML](https://github.com/thomas-young-2013/soln-ml) : an open source system that provides end-to-end ML model training and inference capabilities.
        
        
        ## **License**
        
        The entire codebase is under [MIT license](LICENSE)
        
Platform: UNKNOWN
Requires-Python: >=3.5.2
Description-Content-Type: text/markdown
Provides-Extra: dev
