class Boa(PopulationDistribution):
"""The Bayesian Optimization Algorithm of Martin Pelikan. """
config = Config(
variableGenerator=BinaryVariable,
branchFactor=3,
structureGenerator=GreedyStructureSearch(
3, BayesianInformationCriterion()),
randomizer=lambda net: TernaryString(0L, -1L, net.numVariables),
sampler=DAGSampler(),
data=None,
truncate=0.60, # percentage of the population to keep
space=BinaryReal(realDim=5),
history=SortedMarkovHistory)
def __init__(self, **kwargs):
super(Boa, self).__init__(**kwargs)
self.network = BayesNet(numVariables=self.config.space.dim,
**self.config.__properties__)
self.network = StructureProposal(**self.config.__properties__).search(
self.network)
self.trained = False
def compatible(self, history):
return hasattr(history, "lastPopulation") and history.sorted
def sample(self):
x = self.network.__call__()
cnt = 0
while not self.config.space.in_bounds(x):
if cnt > 10000:
raise ValueError(
"Rejection sampling failed after 10,000 attempts in BOA")
x = self.network.__call__()
cnt += 1
return x
def batch(self, num):
return [self.sample() for i in xrange(num)]
def update(self, history, fitness):
super(Boa, self).update(history, fitness)
if history.lastPopulation() is None:
return
population = [x for x, s in history.lastPopulation()]
selected = int(self.config.truncate * len(population))
self.network = BayesNet(numVariables=self.config.space.dim,
**self.config.__properties__)
self.network.config.data = None
self.network = StructureProposal(
**self.network.config.__properties__).search(self.network)
self.network.config.data = population
self.network.structureSearch(population)
self.network.update(self.history.updates, population)
class Boa(PopulationDistribution):
"""The Bayesian Optimization Algorithm of Martin Pelikan. """
config = Config(variableGenerator=BinaryVariable,
branchFactor=3,
structureGenerator=GreedyStructureSearch(3, BayesianInformationCriterion()),
randomizer = lambda net: TernaryString(0L, -1L, net.numVariables),
sampler = DAGSampler(),
data = None,
truncate = 0.60, # percentage of the population to keep
space = BinaryReal(realDim=5),
history=SortedMarkovHistory)
def __init__(self, **kwargs):
super(Boa, self).__init__(**kwargs)
self.network = BayesNet(numVariables=self.config.space.dim,
**self.config.__properties__)
self.network = StructureProposal(**self.config.__properties__).search(self.network)
self.trained = False
def compatible(self, history):
return hasattr(history, "lastPopulation") and history.sorted
def sample(self):
x = self.network.__call__()
cnt = 0
while not self.config.space.in_bounds(x):
if cnt > 10000:
raise ValueError("Rejection sampling failed after 10,000 attempts in BOA")
x = self.network.__call__()
cnt += 1
return x
def batch(self, num):
return [self.sample() for i in xrange(num)]
def update(self, history, fitness):
super(Boa, self).update(history,fitness)
if history.lastPopulation() is None:
return
population = [x for x,s in history.lastPopulation()]
selected = int(self.config.truncate * len(population))
self.network = BayesNet(numVariables=self.config.space.dim,
**self.config.__properties__)
self.network.config.data = None
self.network = StructureProposal(**self.network.config.__properties__).search(self.network)
self.network.config.data = population
self.network.structureSearch(population)
self.network.update(self.history.updates, population)
