Parameter tuning in Python
from jpype import *
classpath = 'C:\\Program Files\\Bayes Server\\Bayes Server 9.2\\API\\Java\\bayesserver-9.2.jar'
startJVM(getDefaultJVMPath(), '-Djava.class.path=%s' % classpath, convertStrings=False)
bayes = JPackage('com.bayesserver')
bayes_data = bayes.data
bayes_inference = bayes.inference
bayes_analysis = bayes.analysis
network_path = 'C:\\ProgramData\\Bayes Server 8.19\\Sample Networks\\Asia.bayes'
network = bayes.Network()
network.load(network_path)
variables = network.getVariables()
visit_to_asia = variables.get('Visit to Asia', True)
has_lung_cancer = variables.get('Has Lung Cancer', True)
tuberculosis_or_cancer = variables.get('Tuberculosis or Cancer', True)
smoker = variables.get('Smoker', True)
has_tuberculosis = variables.get('Has Tuberculosis', True)
dyspnea = variables.get('Dyspnea', True)
xray_result = variables.get('XRay Result', True)
has_bronchitis = variables.get('Has Bronchitis', True)
xRayResultAbnormal = xray_result.getStates().get('Abnormal', True)
smokerFalse = smoker.getStates().get('False', True)
hasLungCancerFalse = has_lung_cancer.getStates().get('False', True)
evidence = bayes_inference.DefaultEvidence(network)
sensitivity = bayes_analysis.SensitivityToParameters(network, bayes_inference.RelevanceTreeInferenceFactory())
parameters_to_test = []
parameters_to_test.append(
bayes_analysis.ParameterReference(has_lung_cancer.getNode(), [smokerFalse, hasLungCancerFalse]))
print('Node\tParameter\tMin\tMax')
for parameter in parameters_to_test:
oneWay = sensitivity.oneWay(
evidence,
xRayResultAbnormal,
parameter)
try:
output = bayes_analysis.ParameterTuning.oneWaySimple(
oneWay,
bayes.Interval(
java.lang.Double(0.2),
java.lang.Double(0.25),
bayes.IntervalEndPoint.CLOSED,
bayes.IntervalEndPoint.CLOSED))
param_states_text = '[' + ','.join([str(s.getVariable().getName()) + ' = ' + str(s.getName()) for s in parameter.getStates()]) + ']'
print('{}\t{}\t{}\t{}'.format(
parameter.getNode().getName(),
param_states_text,
output.getInterval().getMinimum(),
output.getInterval().getMaximum()
))
except bayes_analysis.ConstraintNotSatisfiedException:
print("Ignoring here as solution not found for this parameter.")