Metadata-Version: 2.1
Name: gfsopt
Version: 1.1.0
Summary: Scaffolding for the Global Function Search optimizer from Dlib
Home-page: https://github.com/tsoernes/gfsopt
Author: Torstein Soernes
Author-email: t.soernes@gmail.com
License: UNKNOWN
Project-URL: Documentation, http://gfsopt.readthedocs.io/
Description: # gfsopt
        [![Documentation Status](https://readthedocs.org/projects/gfsopt/badge/?version=latest)](https://gfsopt.readthedocs.io/en/latest/?badge=latest)
        [![Latest Version](https://pypip.in/version/gfsopt/badge.svg)](https://pypi.python.org/pypi/gfsopt/)
        
        `pip3 install --user gfsopt`
        
        Convenient scaffolding for the excellent
        [Global Function Search](http://dlib.net/optimization.html#global_function_search) 
        hyperparameter optimizer from the [Dlib](http://dlib.net) library. 
        (See: ['A Global Optimization Algorithm Worth Using'](http://blog.dlib.net/2017/12/a-global-optimization-algorithm-worth.html))
        
        Provides the following features:
        * Parallel optimization: Run multiple hyperparameter searches in parallel
        * Save and restore progress: Save/restore settings and optimization progress to/from file. 
        * Average over multiple runs: Run a stochastic objective function using the same
        parameters multiple times and report the average to Dlib's Global Function
        Search. Useful in highly stochastic domains to avoid biasing the search towards
        lucky runs.
        # Example usage
        A basic example where we maximize `obj_func` with respect to `y` over 10 runs,
        with as many parallel processes as there are logical cores, and save progress to file.
        ```
        from gfsopt import GFSOptimizer
        def obj_func(x, y, pid):
            """"Function to be maximized (pid is iteration number)""""
            a = (1.5 - x + x * y)**2
            b = (2.25 - x + x * y * y)**2
            c = (2.625 - x + x * y * y * y)**2
            return -(a + b + c)
        # For this example we pretend that we want to keep 'x' fixed at 0.5
        # while optimizing 'y' in the range -4.5 to 4.5
        pp = {'x': 0.5}  # Problem parameters
        space = {'y': [-4.5, 4.5]}  # Parameters to optimize over
        optimizer = GFSOptimizer(pp, space, fname="test.pkl")
        # Will sample and test 'y' 10 times, then save results, progress and settings to file
        optimizer.run(obj_func, n_sims=10)
        ```
        For a more extensive example, see 
        [example.py](https://github.com/tsoernes/gfsopt/blob/master/example.py).
        
        # Installation & Requirements
        Requires Python >=3.6 and the following libraries:
        ```
        datadiff
        dlib
        numpy
        ```
        
        To install, do:
        
        `pip3 install --user gfsopt`
        
        # Documentation
        See [example.py](https://github.com/tsoernes/gfsopt/blob/master/example.py) for
        an example and [http://gfsopt.readthedocs.io/](http://gfsopt.readthedocs.io/)
        for API documentation.
        
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
