Metadata-Version: 2.1
Name: randopt
Version: 0.2.7
Summary: Streamlined machine learning experiment management.
Home-page: https://github.com/seba-1511/randopt
Author: Seb Arnold
Author-email: smr.arnold@gmail.com
License: License :: OSI Approved :: Apache Software License
Download-URL: https://github.com/seba-1511/randopt/archive/0.2.7.zip
Description: <p align="center"><img src="./assets/images/logo.png" /></p>
        
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        [![Build Status](https://travis-ci.org/seba-1511/randopt.svg?branch=master)](https://travis-ci.org/seba-1511/randopt)
        [![PyPI version](https://badge.fury.io/py/randopt.svg)](https://badge.fury.io/py/randopt)
        
        randopt is a Python package for machine learning experiment management, hyper-parameter optimization, and results visualization. Some of its features include:
        
        * result logging and management,
        * human-readable format,
        * support for parallelism / distributed / asynchronous experiments,
        * command-line and programmatic API,
        * shareable, flexible Web visualization,
        * automatic hyper-parameter search, and
        * pure Python - no dependencies !
        
        
        # Installation
        
        ```shell
        pip install randopt
        ```
        
        # Usage
        
        ```python
        import randopt as ro
        
        def loss(x):
            return x**2
        
        e = ro.Experiment('myexp', {
                'alpha': ro.Gaussian(mean=0.0, std=1.0, dtype='float'),
            })
        
        # Sampling parameters
        for i in xrange(100):
            e.sample('alpha')
            res = loss(e.alpha)
            print('Result: ', res)
            e.add_result(res)
        
        # Manually setting parameters
        e.alpha = 0.00001
        res = loss(e.alpha)
        e.add_result(res)
        
        # Search over all experiments results, including ones from previous runs
        opt = e.minimum()
        print('Best result: ', opt.result, ' with params: ', opt.params)
        ```
        
        ## Results Visualization
        
        Once you obtained some results, run `roviz.py path/to/experiment/folder` to visualize them in your web browser.
        
        For more info on visualization and `roviz.py`, refer to the [Visualizing Results](https://github.com/seba-1511/randopt/wiki/Visualizing-Results) tutorial.
        
        ## Hyper-Parameter Optimization
        
        To generate results and search for good hyper-parameters you can either user `ropt.py` or write your own optimizaiton script using the [Evolutionary](https://github.com/seba-1511/randopt/wiki/evolutionary) and [GridSearch](https://github.com/seba-1511/randopt/wiki/grid_search) classes.
        
        For more info on hyper-parameter optimization, refer to the [Optimizing Hyperparams](https://github.com/seba-1511/randopt/wiki/Optimizing-Hyperparams) tutorial.
        
        
        # Documentation
        
        For more examples, tutorials, and documentation refer to the [wiki](https://github.com/seba-1511/randopt/wiki).
        
        
        # Contributing
        
        To contribute to Randopt, it is recommended to follow the [contribution guidelines](CONTRIBUTING.md).
        
        ### Acknowledgements
        Randopt is maintained by [Séb Arnold](http://seba1511.com), with numerous contributions from the following persons.
        
        * Noel Trivedi
        * Cyrus Jia
        * Daler Asrorov
        
        # License
        
        Randopt is released under the Apache 2 License. For more information, refer to the [LICENSE file](LICENSE.txt).
        
        I would love to hear how your use Randopt. Feel free to [drop me a line](http://seba1511.com) !
        
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