('+', 'fail'):[.05, .95],
('-', 'pass'):[.7, .3],
('-', 'fail'):[0, 1]
}))
academe_graph = DiscreteBayesNet([theory, practice, extra, others, markTP, others, globalMark, finalMark])
# This is designed to reproduce the results of Flores' paper
print "Testing Explanation Tree:"
test_tree = generate_explanation_tree(academe_graph, ['Theory', 'Practice', 'Extra', 'Others'], {'finalMark':'fail'}, [], 0.0001, 0.0002)
print test_tree
print "========================="
print "Testing MRE:"
MRE = generate_MRE(academe_graph, ['Theory', 'Practice', 'Extra', 'Others'], {'finalMark':'fail'})
for n in MRE:
print n
print "========================="
print "Testing K-MRE:"
K_MRE = generate_K_MRE(MRE)
print K_MRE
print "========================="
print "Testing MAP:"
MRE = generate_MAP_Ind_Simplification(academe_graph, ['Theory', 'Practice', 'Extra', 'Others'], {'finalMark':'fail'}, 0.1)
for x in MRE:
print x
print "========================="
lake_graph.prob({'Bird':'T','Island':'F' })
#test running explanation tree algorithm on the above graph
print "Testing Explanation Tree:"
test_tree = generate_explanation_tree(lake_graph, ['Bird', 'Island'], {'Pox':'T'}, [], 0.0001, 0.0002)
print test_tree
print "========================="
print "Testing Explanation Forest:"
forest = generate_ET_forest(lake_graph, ['Bird', 'Island'], {'Pox':'T'}, [])
for tree in forest:
print tree
print "========================="
print "Testing MRE:"
MRE = generate_MRE(lake_graph, ['Bird', 'Island'], {'Pox':'T'})
for x in MRE:
print x
print "========================="
print "Testing K-MRE:"
K_MRE = generate_K_MRE(MRE)
for x in K_MRE:
print x
print "========================="
print "Testing Causal Intervention:"
print "Intervene Bird to true"
intervened = lake_graph.create_graph_with_intervention({'Bird' : 'T'})
print "post intervention probability of Bird being true", intervened.prob( {'Bird' : 'T'} )
print "post intervention prob of Pox being true", intervened.prob( {'Pox':'T'} )
('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 running explanation tree algorithm on the above graph
print "Testing Explanation Tree:"
test_tree = generate_explanation_tree(lake_graph, ['Bird', 'Island'],
{'Pox': 'T'}, [], 0.0001, 0.0002)
print test_tree
print "========================="
print "Testing Explanation Forest:"
forest = generate_ET_forest(lake_graph, ['Bird', 'Island'], {'Pox': 'T'}, [])
for tree in forest:
print tree
print "========================="
print "Testing MRE:"
MRE = generate_MRE(lake_graph, ['Bird', 'Island'], {'Pox': 'T'})
for x in MRE:
print x
print "========================="
print "Testing K-MRE:"
K_MRE = generate_K_MRE(MRE)
for x in K_MRE:
print x
print "========================="
print "Testing Causal Intervention:"
print "Intervene Bird to true"
intervened = lake_graph.create_graph_with_intervention({'Bird': 'T'})
print "post intervention probability of Bird being true", intervened.prob(
{'Bird': 'T'})
dyspnea = DiscreteBayesNode(
"Dyspnea",
["TborCa", "Bronchitis"],
DiscreteCPT(
["yes", "no"],
{("yes", "yes"): [0.9, 0.1], ("yes", "no"): [0.7, 0.3], ("no", "yes"): [0.8, 0.2], ("no", "no"): [0.1, 0.9]},
),
)
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)
DiscreteCPT(['f', 'm'], [.5, .5]))
drug = DiscreteBayesNode('Drug', ['Sex'], \
DiscreteCPT(['yes', 'no'],
{('f', ):[.25, .75],
('m', ):[.75, .25]}))
recovery = DiscreteBayesNode('Recovery', ['Sex', 'Drug'], \
DiscreteCPT(['recovery', 'death'],
{
('f', 'yes'):[.2, .8],
('f', 'no'):[.3, .7],
('m', 'yes'):[.6, .4],
('m', 'no'):[.7, .3]
}))
drug_graph = DiscreteBayesNet( [sex, drug, recovery])
print "Testing Explanation Tree:"
test_tree = generate_explanation_tree(drug_graph, ['Sex', 'Drug'], {'Recovery':'recovery'}, [], 0.01, 0.2)
print test_tree
print "========================="
print "Testing MRE:"
MRE = generate_MRE(drug_graph, ['Sex', 'Drug'], {'Recovery':'recovery'})
print MRE
print "========================="
print "Testing Causal Explanation Tree:"
test_tree = generate_causal_explanation_tree(drug_graph, drug_graph, ['Sex', 'Drug'], {},{'Recovery':'recovery'}, [], 0.001)
print test_tree
print "========================="
}))
academe_graph = DiscreteBayesNet(
[theory, practice, extra, others, markTP, others, globalMark, finalMark])
# This is designed to reproduce the results of Flores' paper
print "Testing Explanation Tree:"
test_tree = generate_explanation_tree(
academe_graph, ['Theory', 'Practice', 'Extra', 'Others'],
{'finalMark': 'fail'}, [], 0.0001, 0.0002)
print test_tree
print "========================="
print "Testing MRE:"
MRE = generate_MRE(academe_graph, ['Theory', 'Practice', 'Extra', 'Others'],
{'finalMark': 'fail'})
for n in MRE:
print n
print "========================="
print "Testing K-MRE:"
K_MRE = generate_K_MRE(MRE)
print K_MRE
print "========================="
print "Testing MAP:"
MRE = generate_MAP_Ind_Simplification(
academe_graph, ['Theory', 'Practice', 'Extra', 'Others'],
{'finalMark': 'fail'}, 0.1)
for x in MRE:
