#! /usr/bin/env python
# Copyright (c) 2014 KU Leuven, ESAT-STADIUS
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import operator as op
import random
from ..functions import static_key_order
from .solver_registry import register_solver
from .util import Solver, _copydoc, shrink_bounds, uniform_in_bounds
@register_solver('random search',
'random parameter tuples sampled uniformly within box constraints',
['Tests random parameter tuples sampled uniformly within the box constraints.',
' ',
'This function requires the following arguments:',
'- num_evals :: number of tuples to test',
'- box constraints via keywords: constraints are lists [lb, ub]',
' ',
'This solver performs num_evals function evaluations.',
' ',
'This solver implements the technique described here:',
'Bergstra, James, and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research 13 (2012): 281-305.']
)
[docs]class RandomSearch(Solver):
"""
.. include:: /global.rst
Please refer to |randomsearch| for more details about this algorithm.
"""
def __init__(self, num_evals, **kwargs):
"""Initializes the solver with bounds and a number of allowed evaluations.
kwargs must be a dictionary of parameter-bound pairs representing the box constraints.
Bounds are a 2-element list: [lower_bound, upper_bound].
>>> s = RandomSearch(x=[0, 1], y=[-1, 2], num_evals=50)
>>> s.bounds['x']
[0, 1]
>>> s.bounds['y']
[-1, 2]
>>> s.num_evals
50
"""
assert all([len(v) == 2 and v[0] <= v[1]
for v in kwargs.values()]), 'kwargs.values() are not [lb, ub] pairs'
self._bounds = kwargs
self._num_evals = num_evals
@staticmethod
[docs] def suggest_from_box(num_evals, **kwargs):
"""Creates a GridSearch solver that uses ``num_evals`` evaluations
within given bounds (lb, ub). The bounds are first tightened, resulting in
new bounds covering 99% of the area.
>>> s = RandomSearch.suggest_from_box(30, x=[0, 1], y=[-1, 0], z=[-1, 1])
>>> s['x'] #doctest:+SKIP
[0.005, 0.995]
>>> s['y'] #doctest:+SKIP
[-0.995, -0.005]
>>> s['z'] #doctest:+SKIP
[-0.99, 0.99]
>>> s['num_evals']
30
"""
d = shrink_bounds(kwargs)
d['num_evals'] = num_evals
return d
@property
[docs] def upper(self, par):
"""Returns the upper bound of par."""
return self._bounds[par][1]
@property
[docs] def lower(self, par):
"""Returns the lower bound of par."""
return self._bounds[par][0]
@property
[docs] def bounds(self):
"""Returns a dictionary containing the box constraints."""
return self._bounds
@property
[docs] def num_evals(self):
"""Returns the number of evaluations this solver may do."""
return self._num_evals
@_copydoc(Solver.optimize)
[docs] def optimize(self, f, maximize=True, pmap=map):
def generate_rand_args(len=1):
# return [uniform_in_bounds(self.bounds)]
return [[random.uniform(bounds[0], bounds[1]) for _ in range(len)]
for _, bounds in sorted(self.bounds.items())]
best_pars = None
f = static_key_order(self.bounds.keys())(f)
if maximize:
comp = lambda score, best: score > best
else:
comp = lambda score, best: score < best
tuples = generate_rand_args(self.num_evals)
scores = pmap(f, *tuples)
if maximize:
comp = max
else:
comp = min
best_idx, _ = comp(enumerate(scores), key=op.itemgetter(1))
best_pars = op.itemgetter(best_idx)(list(zip(*tuples)))
return dict([(k, v) for k, v in zip(self.bounds.keys(), best_pars)]), None
