Source code for optunity.solvers.util

#! /usr/bin/env python

# Copyright (c) 2014 KU Leuven, ESAT-STADIUS
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
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# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
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# 3. Neither name of copyright holders nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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import abc
import random

def uniform_in_bounds(bounds):
    """Generates a random uniform sample between ``bounds``.

    :param bounds: the bounds we must adhere to
    :type bounds: dict {"name": [lb ub], ...}
    """
    return map(random.uniform, *zip(*bounds.values()))

# python version-independent metaclass usage
SolverBase = abc.ABCMeta('SolverBase', (object, ), {})

class Solver(SolverBase):
    """Base class of all Optunity solvers.
    """

    @abc.abstractmethod
    def optimize(self, f, maximize=True, pmap=map):
        """Optimizes ``f``.

        :param f: the objective function
        :type f: callable
        :param maximize: do we want to maximizes?
        :type maximize: boolean
        :param pmap: the map() function to use
        :type pmap: callable
        :returns:
            - the arguments which optimize ``f``
            - an optional solver report, can be None

        """
        pass

    def maximize(self, f, pmap=map):
        """Maximizes f.

        :param f: the objective function
        :type f: callable
        :param pmap: the map() function to use
        :type pmap: callable
        :returns:
            - the arguments which optimize ``f``
            - an optional solver report, can be None

        """
        return self.optimize(f, True, pmap=pmap)

    def minimize(self, f, pmap=map):
        """Minimizes ``f``.

        :param f: the objective function
        :type f: callable
        :param pmap: the map() function to use
        :type pmap: callable
        :returns:
            - the arguments which optimize ``f``
            - an optional solver report, can be None

        """
        return self.optimize(f, False, pmap=pmap)


# http://stackoverflow.com/a/13743316
def _copydoc(fromfunc, sep="\n"):
    """
    Decorator: Copy the docstring of `fromfunc`
    """
    def _decorator(func):
        sourcedoc = fromfunc.__doc__
        if func.__doc__ == None:
            func.__doc__ = sourcedoc
        else:
            func.__doc__ = sep.join([sourcedoc, func.__doc__])
        return func
    return _decorator

def shrink_bounds(bounds, coverage=0.99):
    """Shrinks the bounds. The new bounds will cover the fraction ``coverage``.

    >>> [round(x, 3) for x in shrink_bounds([0, 1], coverage=0.99)]
    [0.005, 0.995]

    """
    def shrink(lb, ub, coverage):
        new_range = float(ub-lb)*coverage/2
        middle = float(ub+lb)/2
        return [middle-new_range, middle+new_range]

    return dict([(k, shrink(v[0], v[1], coverage))
                 for k, v in bounds.items()])