#! /usr/bin/env python
# Copyright (c) 2014 KU Leuven, ESAT-STADIUS
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from .solver_registry import register_solver
from .util import Solver, _copydoc
import functools
import random
_numpy_available = True
try:
import numpy
except ImportError:
_numpy_available = False
_hyperopt_available = True
try:
import hyperopt
except ImportError:
_hyperopt_available = False
[docs]class TPE(Solver):
"""
.. include:: /global.rst
This solver implements the Tree-structured Parzen Estimator, as described in [TPE2011]_.
This solver uses Hyperopt in the back-end and exposes the TPE estimator with uniform priors.
Please refer to |tpe| for details about this algorithm.
.. [TPE2011] Bergstra, James S., et al. "Algorithms for hyper-parameter optimization." Advances in Neural Information Processing Systems. 2011
"""
def __init__(self, num_evals=100, seed=None, **kwargs):
"""
Initialize the TPE solver.
:param num_evals: number of permitted function evaluations
:type num_evals: int
:param seed: the random seed to be used
:type seed: double
:param kwargs: box constraints for each hyperparameter
:type kwargs: {'name': [lb, ub], ...}
"""
if not _hyperopt_available:
raise ImportError('This solver requires Hyperopt but it is missing.')
if not _numpy_available:
raise ImportError('This solver requires NumPy but it is missing.')
self._seed = seed
self._bounds = kwargs
self._num_evals = num_evals
@staticmethod
[docs] def suggest_from_box(num_evals, **kwargs):
"""
Verify that we can effectively make a solver from box.
>>> s = TPE.suggest_from_box(30, x=[0, 1], y=[-1, 0], z=[-1, 1])
>>> solver = TPE(**s) #doctest:+SKIP
"""
d = dict(kwargs)
d['num_evals'] = num_evals
return d
@property
def seed(self):
return self._seed
@property
def bounds(self):
return self._bounds
@property
def num_evals(self):
return self._num_evals
@_copydoc(Solver.optimize)
[docs] def optimize(self, f, maximize=True, pmap=map):
if maximize:
def obj(args):
kwargs = dict([(k, v) for k, v in zip(self.bounds.keys(), args)])
return -f(**kwargs)
else:
def obj(args):
kwargs = dict([(k, v) for k, v in zip(self.bounds.keys(), args)])
return f(**kwargs)
seed = self.seed if self.seed else random.randint(0, 9999999999)
algo = functools.partial(hyperopt.tpe.suggest, seed=seed)
space = [hyperopt.hp.uniform(k, v[0], v[1]) for k, v in self.bounds.items()]
best = hyperopt.fmin(obj, space=space, algo=algo, max_evals=self.num_evals)
return best, None
# TPE is a simple wrapper around Hyperopt's TPE solver
if _hyperopt_available and _numpy_available:
TPE = register_solver('TPE', 'Tree of Parzen estimators',
['TPE: Tree of Parzen Estimators']
)(TPE)