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 "========================="
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'} )
print "========================="
print "Testing Causal Explanation Tree:"
test_tree = generate_causal_explanation_tree(lake_graph, lake_graph, ['Bird', 'Island'], {}, {'Pox':'T'}, [], 0.0001)
print test_tree
print "========================="
print "Testing Explanation Forest:"
forest = generate_CET_forest(lake_graph, lake_graph, ['Bird', 'Island'], {}, {'Pox':'T'}, [])
for tree in forest:
print tree
print "========================="
print "Testing scores calculations of different methods:"
print "BGF of [Island being true]: ", calculate_GBF( lake_graph, {"Island" : "T" }, {"Pox" : "T"} )
print "BGF of [Bird being true, Island being false]: ", calculate_GBF( lake_graph, {"Island" : "T", "Bird" : "T" }, {"Pox" : "T"} )
print "ET score of [Island being true], which is essentially posterior probability of the explanation given explanadum : ", calculate_ET_score( lake_graph, {"Island" : "T"}, {"Pox" : "T"})
("off", "on", "off"): [1, 0],
("off", "on", "on"): [1, 0],
("off", "off", "on"): [1, 0],
("off", "off", "off"): [0, 1],
},
),
)
cur_input = DiscreteBayesNode("Input", [], DiscreteCPT(["on", "off"], [1, 0]))
circuit_graph = DiscreteBayesNet([a, b, c, d, a_out, b_out, c_out, d_out, out, cur_input])
print "Testing Causal Explanation Tree:"
test_tree = generate_causal_explanation_tree(
circuit_graph, circuit_graph, ["A_switch", "B_switch", "C_switch", "D_switch"], {}, {"Output": "on"}, [], 0.001
)
print test_tree
print "========================="
# print "Testing Explanation Tree:"
# test_tree = generate_explanation_tree(circuit_graph, ['A_switch', 'B_switch', 'C_switch', 'D_switch'], {'Output':'on'}, [], 0.01, 0.2)
# print test_tree
# print "========================="
# print "Testing MRE:"
# MRE = generate_MRE(circuit_graph, ['A_switch', 'B_switch', 'C_switch', 'D_switch'], {'Output':'on', 'Input':'on'})
# for x in MRE:
# print x
# #print 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'})
print "========================="
print "Testing Causal Explanation Tree:"
test_tree = generate_causal_explanation_tree(lake_graph, lake_graph,
['Bird', 'Island'], {},
{'Pox': 'T'}, [], 0.0001)
print test_tree
print "========================="
print "Testing Explanation Forest:"
forest = generate_CET_forest(lake_graph, lake_graph, ['Bird', 'Island'], {},
{'Pox': 'T'}, [])
for tree in forest:
print tree
print "========================="
print "Testing scores calculations of different methods:"
print "BGF of [Island being true]: ", calculate_GBF(lake_graph,
{"Island": "T"},
{"Pox": "T"})
["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)
print test_tree
print "========================="
def fill_in_csv(graph, exp_var, explanadum, path = "temp.csv", lake = 0):
# Assume for now that the codes in csv use the some scheme as implied
# by this script
assignments = []
code_hash = encode_nodes_to_hash([graph.variables[name] for name in exp_var])
MPE_space = generate_MPE(graph, exp_var, explanadum)
MAP_threshold = 0.1
MAP_space = generate_MAP_Ind_Simplification(graph, exp_var, explanadum, MAP_threshold)
MRE_space = generate_MRE(graph, exp_var, explanadum)
alpha = 0.01
beta = 0.2
ET = generate_explanation_tree(graph, exp_var, explanadum, [], alpha, beta)
ET_space = sorted(ET.assignment_space(), key = lambda x: -x[1])
ET_score_space = sorted(
[(code_hash[key], calculate_ET_score(graph, code_hash[key], explanadum)) for key in code_hash.keys() if len(code_hash[key])],
key = lambda x: -x[1])
# print ET_score_space
alpha_CET = 0.0001
CET = generate_causal_explanation_tree(graph, graph, exp_var, {}, explanadum, [], alpha_CET)
# print "Printing cet", CET
CET_space = sorted(CET.assignment_space(), key = lambda x: -x[1])
CET_score_space = sorted(
[(code_hash[key], calculate_CET_score(graph, code_hash[key], {}, explanadum)) for key in code_hash.keys() if len(code_hash[key])],
key = lambda x: -x[1] if not math.isnan(x[1]) else 9999999999999)
# print CET_score_space
with open(path, 'r') as csvfile:
reader = csv.reader(csvfile, dialect = 'excel')
for row in reader:
processed_code = join([row[0][i] for i in range(len(exp_var))], "")
assignments.append((row[0], code_hash[processed_code]))
# print assignments
with open(path, 'w') as csvfile:
writer = csv.writer(csvfile, dialect = 'excel')
writer.writerow(["CondensedString","MPE_rank","MPE_score","MAP_I_rank","MAP_I_score","MAP_I_para_theta","MRE_rank","MRE_score","ET_rank_for_tree","ET_rank_for_score","ET_score","ET_leaf","ET_ALPHA","ET_BETA","CET_rank_for_tree","CET_rank_for_score","CET_score","CET_leaf","CET_ALPHA"])
for code, assignment in assignments:
row = ['"' + code + '"']
# escape all empty explanations
# and all invalid assignemnts for lake.py
print lake, code[2] != '9', code[3] != '9'
if (not len(assignment)) or (lake and (code[2] != '9' or code[3] != '9')):
print "escaping"
row += ["NaN" for _ in range(18)]
writer.writerow(row)
continue
rank = space_rank(MPE_space, assignment)
if rank:
row.append(rank)# MPE rank
row.append(MPE_space[int(rank) - 1][1]) # MPE score
else:
row.append("NaN")
row.append("NaN")
rank = space_rank(MAP_space, assignment)
if rank:
row.append(rank)# map rank
row.append(MAP_space[int(rank) - 1][1]) # MAP score
else:
row.append("NaN")
row.append("NaN")
row.append(MAP_threshold)
rank = space_rank(MRE_space, assignment)
if rank:
row.append(rank)# mre rank
row.append(MRE_space[int(rank) - 1][1]) # MRE score
else:
row.append("NaN")
row.append("NaN")
rank = space_rank(ET_space, assignment)
if rank:
row.append(rank)# et tree rank
else:
row.append("NaN")
rank = space_rank(ET_score_space, assignment)
# print code, assignment, rank, ET_score_space[int(rank) - 1][1]
if rank:
row.append(rank) # et score rank
row.append(ET_score_space[int(rank) - 1][1]) # ET score
else:
row.append("ERROR ! should not happen") #should not happen
row += [1] if ET.is_leaf(assignment) else [0]
row.append(alpha)
row.append(beta)
rank = space_rank(CET_space, assignment)
if rank:
row.append(rank)# et tree rank
else:
row.append("NaN")
rank = space_rank(CET_score_space, assignment)
if rank:
row.append(rank) # et score rank
row.append(CET_score_space[int(rank) - 1][1]) # ET score
else:
row.append("ERROR ! should not happen") #should not happen
# print "determining leaf for", code
row += [1] if CET.is_leaf(assignment) else [0]
row.append(alpha_CET)
writer.writerow(row)
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 "========================="
('on', 'off', 'on') : [1, 0],
('on', 'off', 'off') : [1, 0],
('off', 'on', 'off') : [1, 0],
('off', 'on', 'on') : [1, 0],
('off', 'off', 'on') : [1, 0],
('off', 'off', 'off') : [0, 1],
}))
cur_input = DiscreteBayesNode('Input', [], DiscreteCPT(['on', 'off'], [1, 0]))
circuit_graph = DiscreteBayesNet(
[a, b, c, d, a_out, b_out, c_out, d_out, out, cur_input])
print "Testing Causal Explanation Tree:"
test_tree = generate_causal_explanation_tree(
circuit_graph, circuit_graph,
['A_switch', 'B_switch', 'C_switch', 'D_switch'], {}, {'Output': 'on'}, [],
0.001)
print test_tree
print "========================="
# print "Testing Explanation Tree:"
# test_tree = generate_explanation_tree(circuit_graph, ['A_switch', 'B_switch', 'C_switch', 'D_switch'], {'Output':'on'}, [], 0.01, 0.2)
# print test_tree
# print "========================="
# print "Testing MRE:"
# MRE = generate_MRE(circuit_graph, ['A_switch', 'B_switch', 'C_switch', 'D_switch'], {'Output':'on', 'Input':'on'})
# for x in MRE:
# print x
# #print MRE
# print "========================="
def fill_in_csv(graph, exp_var, explanadum, path="temp.csv", lake=0):
# Assume for now that the codes in csv use the some scheme as implied
# by this script
assignments = []
code_hash = encode_nodes_to_hash(
[graph.variables[name] for name in exp_var])
MPE_space = generate_MPE(graph, exp_var, explanadum)
MAP_threshold = 0.1
MAP_space = generate_MAP_Ind_Simplification(graph, exp_var, explanadum,
MAP_threshold)
MRE_space = generate_MRE(graph, exp_var, explanadum)
alpha = 0.01
beta = 0.2
ET = generate_explanation_tree(graph, exp_var, explanadum, [], alpha, beta)
ET_space = sorted(ET.assignment_space(), key=lambda x: -x[1])
ET_score_space = sorted([
(code_hash[key], calculate_ET_score(graph, code_hash[key], explanadum))
for key in code_hash.keys() if len(code_hash[key])
],
key=lambda x: -x[1])
# print ET_score_space
alpha_CET = 0.0001
CET = generate_causal_explanation_tree(graph, graph, exp_var, {},
explanadum, [], alpha_CET)
# print "Printing cet", CET
CET_space = sorted(CET.assignment_space(), key=lambda x: -x[1])
CET_score_space = sorted(
[(code_hash[key],
calculate_CET_score(graph, code_hash[key], {}, explanadum))
for key in code_hash.keys() if len(code_hash[key])],
key=lambda x: -x[1] if not math.isnan(x[1]) else 9999999999999)
# print CET_score_space
with open(path, 'r') as csvfile:
reader = csv.reader(csvfile, dialect='excel')
for row in reader:
processed_code = join([row[0][i] for i in range(len(exp_var))], "")
assignments.append((row[0], code_hash[processed_code]))
# print assignments
with open(path, 'w') as csvfile:
writer = csv.writer(csvfile, dialect='excel')
writer.writerow([
"CondensedString", "MPE_rank", "MPE_score", "MAP_I_rank",
"MAP_I_score", "MAP_I_para_theta", "MRE_rank", "MRE_score",
"ET_rank_for_tree", "ET_rank_for_score", "ET_score", "ET_leaf",
"ET_ALPHA", "ET_BETA", "CET_rank_for_tree", "CET_rank_for_score",
"CET_score", "CET_leaf", "CET_ALPHA"
])
for code, assignment in assignments:
row = ['"' + code + '"']
# escape all empty explanations
# and all invalid assignemnts for lake.py
print lake, code[2] != '9', code[3] != '9'
if (not len(assignment)) or (lake and
(code[2] != '9' or code[3] != '9')):
print "escaping"
row += ["NaN" for _ in range(18)]
writer.writerow(row)
continue
rank = space_rank(MPE_space, assignment)
if rank:
row.append(rank) # MPE rank
row.append(MPE_space[int(rank) - 1][1]) # MPE score
else:
row.append("NaN")
row.append("NaN")
rank = space_rank(MAP_space, assignment)
if rank:
row.append(rank) # map rank
row.append(MAP_space[int(rank) - 1][1]) # MAP score
else:
row.append("NaN")
row.append("NaN")
row.append(MAP_threshold)
rank = space_rank(MRE_space, assignment)
if rank:
row.append(rank) # mre rank
row.append(MRE_space[int(rank) - 1][1]) # MRE score
else:
row.append("NaN")
row.append("NaN")
rank = space_rank(ET_space, assignment)
if rank:
row.append(rank) # et tree rank
else:
row.append("NaN")
rank = space_rank(ET_score_space, assignment)
# print code, assignment, rank, ET_score_space[int(rank) - 1][1]
if rank:
row.append(rank) # et score rank
row.append(ET_score_space[int(rank) - 1][1]) # ET score
else:
row.append("ERROR ! should not happen") #should not happen
row += [1] if ET.is_leaf(assignment) else [0]
row.append(alpha)
row.append(beta)
rank = space_rank(CET_space, assignment)
if rank:
row.append(rank) # et tree rank
else:
row.append("NaN")
rank = space_rank(CET_score_space, assignment)
if rank:
row.append(rank) # et score rank
row.append(CET_score_space[int(rank) - 1][1]) # ET score
else:
row.append("ERROR ! should not happen") #should not happen
# print "determining leaf for", code
row += [1] if CET.is_leaf(assignment) else [0]
row.append(alpha_CET)
writer.writerow(row)