def setUp(self):
config.StringParameter('test.param0', 'a param', default='foo')
config.StringParameter('test.param1', 'a param', config.oneof('foo', 'bar'))
config.IntParameter('test.param2', 'a param', default=20)
config.IntParameter('test.param3', 'a param', config.atmost(100))
config.IntParameter('test.param4', 'a param', config.atleast(100))
config.IntParameter('test.param5', 'a param', config.between(10,100))
config.IntParameter('test.param6', 'a param', lambda x: x == 50)
config.FloatParameter('test.param7', 'a param', config.between(1.3, 2.7))
def setUp(self):
config.StringParameter('test.param0', 'a param', default='foo')
config.StringParameter('test.param1', 'a param',
config.oneof('foo', 'bar'))
config.IntParameter('test.param2', 'a param', default=20)
config.IntParameter('test.param3', 'a param', config.atmost(100))
config.IntParameter('test.param4', 'a param', config.atleast(100))
config.IntParameter('test.param5', 'a param', config.between(10, 100))
config.IntParameter('test.param6', 'a param', lambda x: x == 50)
config.FloatParameter('test.param7', 'a param',
config.between(1.3, 2.7))
class SimulatedAnnealingLearner(Learner):
#
# Parameters
#
_params = (
config.FloatParameter(
'simanneal.start_temp',
"Starting temperature for a run.",
config.atleast(0.0),
default=100.0
),
config.FloatParameter(
'simanneal.delta_temp',
'Change in temp between steps.',
config.atleast(0.0),
default=0.5
),
config.IntParameter(
'simanneal.max_iters_at_temp',
'Max iterations at any temperature.',
config.atleast(0),
default=100
),
config.StringParameter(
'simanneal.seed',
'Starting network for a greedy search.',
default=''
)
)
def __init__(self, data_=None, prior_=None, **options):
"""Create a Simulated Aneaaling learner.
For more information about Simulated Annealing algorithms, consult:
1. http://en.wikipedia.org/wiki/Simulated_annealing
2. D. Heckerman. A Tutorial on Learning with Bayesian Networks.
Microsoft Technical Report MSR-TR-95-06, 1995. p.35-36.
Any config param for 'simanneal' can be passed in via options.
Use just the option part of the parameter name.
"""
super(SimulatedAnnealingLearner,self).__init__(data_, prior_)
config.setparams(self, options)
if not isinstance(self.seed, network.Network):
self.seed = network.Network(self.data.variables, self.seed)
def run(self):
"""Run the learner."""
self.stats = SALearnerStatistics(self.start_temp, self.delta_temp,
self.max_iters_at_temp)
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, self.seed, self.prior)
self.evaluator.score_network(self.seed.copy())
self.result.start_run()
curscore = self.evaluator.score_network()
# temperature decays exponentially, so we'll never get to 0.
# So, we continue until temp < 1
while self.stats.temp >= 1:
try:
newscore = self._alter_network_randomly_and_score()
except CannotAlterNetworkException:
return
self.result.add_network(self.evaluator.network, newscore)
if self._accept(newscore):
# set current score
self.stats.current_score = newscore
if self.stats.current_score > self.stats.best_score:
self.stats.best_score = self.stats.current_score
else:
# undo network alteration
self.evaluator.restore_network()
# temp not updated EVERY iteration. just whenever criteria met.
self.stats.update()
self.result.stop_run()
return self.result
def _accept(self, newscore):
oldscore = self.stats.current_score
if newscore >= oldscore:
return True
elif random.random() < exp((newscore - oldscore)/self.stats.temp):
return True
else:
return False
_plearnertype = config.StringParameter(
'learner.type',
"""Type of learner to use.
The following learners are included with pebl:
* greedy.GreedyLearner
* simanneal.SimulatedAnnealingLearner
* exhaustive.ListLearner
""",
default = 'greedy.GreedyLearner'
)
_ptasks = config.IntParameter(
'learner.numtasks',
"Number of learner tasks to run.",
config.atleast(0),
default=1
)
class Learner(Task):
def __init__(self, data_=None, prior_=None, **kw):
self.data = data_ or data.fromconfig()
self.prior = prior_ or prior.fromconfig()
self.__dict__.update(kw)
# parameters
self.numtasks = config.get('learner.numtasks')
# stats
self.reverse = 0
