/usr/share/pyshared/pebl/learner/simanneal.py is in python-pebl 1.0.2-2.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | """Classes and functions for Simulated Annealing learner"""
from math import exp
import random
from pebl import network, result, evaluator, config
from pebl.learner.base import *
class SALearnerStatistics:
def __init__(self, starting_temp, delta_temp, max_iterations_at_temp):
self.temp = starting_temp
self.iterations_at_temp = 0
self.max_iterations_at_temp = max_iterations_at_temp
self.delta_temp = delta_temp
self.iterations = 0
self.best_score = 0
self.current_score = 0
def update(self):
self.iterations += 1
self.iterations_at_temp += 1
if self.iterations_at_temp >= self.max_iterations_at_temp:
self.temp *= self.delta_temp
self.iterations_at_temp = 0
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
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