forked from neocxi/Explanation-Engine
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asia.py
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asia.py
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from bayesnet import DiscreteBayesNode, DiscreteCPT, DiscreteBayesNet
from explanation_tree import generate_explanation_tree
from most_relevant_explanation import generate_MRE, generate_K_MRE
from causal_explanation_tree import generate_causal_explanation_tree
asia = DiscreteBayesNode('VisitToAsia', [], \
DiscreteCPT(['yes', 'no'], [.01, .99]))
smoking = DiscreteBayesNode('Smoking', [], \
DiscreteCPT(['yes','no'], [.5, .5]))
tuberculosis = DiscreteBayesNode('Tuberculosis', ['VisitToAsia'], \
DiscreteCPT(['yes', 'no'],
{
('yes', ) : [.05, .95],
('no', ) : [.01, .99]
}))
lung_cancer = DiscreteBayesNode('Lung_Cancer', ['Smoking'], \
DiscreteCPT(['yes', 'no'],
{
('yes', ) : [.1, .9],
('no', ) : [.01, .99]
}))
bronchitis = DiscreteBayesNode('Bronchitis', ['Smoking'], \
DiscreteCPT(['yes', 'no'],
{
('yes', ) : [.6, .4],
('no', ) : [.3, .7]
}))
tborca = DiscreteBayesNode('TborCa', ['Tuberculosis', 'Lung_Cancer'], \
DiscreteCPT(['yes', 'no'],
{
('yes', 'yes') : [1, 0],
('yes', 'no') : [1, 0],
('no', 'yes') : [1, 0],
('no', 'no') : [0, 1]
}))
x_ray = DiscreteBayesNode('X_ray', ['TborCa'], \
DiscreteCPT(['abnormal', 'normal'],
{
('yes', ) : [.98, .02],
('no', ) : [.05, .95]
}) )
dyspnea = DiscreteBayesNode('Dyspnea', ['TborCa', 'Bronchitis'], \
DiscreteCPT(['yes', 'no'],
{
('yes', 'yes') : [.9, .1],
('yes', 'no') : [.7, .3],
('no', 'yes') : [.8, .2],
('no', 'no') : [.1, .9]
}))
asia_graph = DiscreteBayesNet( [asia, tuberculosis, tborca, x_ray, dyspnea, bronchitis, smoking, lung_cancer] )
exp_var = ['Lung_Cancer', 'VisitToAsia', 'Tuberculosis', 'Smoking', 'Bronchitis']
explanadum = {'X_ray':'abnormal'}
print "Testing MRE:"
MRE = generate_MRE(asia_graph, exp_var, explanadum)
print MRE
print "========================="
print "Testing K-MRE:"
K_MRE = generate_K_MRE(MRE)
print K_MRE
print "========================="
print "Testing Explanation Tree:"
test_tree = generate_explanation_tree(asia_graph, exp_var, explanadum, [], 0.01, 0.2)
print test_tree
print "========================="
print "Testing Causal Explanation Tree:"
test_tree = generate_causal_explanation_tree(asia_graph, asia_graph, exp_var, {},explanadum, [], 0.001)
print test_tree
print "========================="