def test_map_query_with_evidence(self):
belief_propagation = BeliefPropagation(self.bayesian_model)
map_query = belief_propagation.map_query(['A', 'R', 'L'], {
'J': 0,
'Q': 1,
'G': 0
})
self.assertDictEqual(map_query, {'A': 1, 'R': 0, 'L': 0})
def test_map_query_with_evidence(self):
belief_propagation = BeliefPropagation(self.bayesian_model)
map_query = belief_propagation.map_query(["A", "R", "L"], {
"J": 0,
"Q": 1,
"G": 0
})
self.assertDictEqual(map_query, {"A": 1, "R": 0, "L": 0})
def test_map_query(self):
belief_propagation = BeliefPropagation(self.bayesian_model)
map_query = belief_propagation.map_query()
self.assertDictEqual(map_query, {
'A': 1,
'R': 1,
'J': 1,
'Q': 1,
'G': 0,
'L': 0
})
def test_map_query(self):
belief_propagation = BeliefPropagation(self.bayesian_model)
map_query = belief_propagation.map_query()
self.assertDictEqual(map_query, {
"A": 1,
"R": 1,
"J": 1,
"Q": 1,
"G": 0,
"L": 0
})
ITEM = random.sample(ND_Sample_N.index.tolist(), 1)
# vbs = ['SL32','SC22','SF1','SC33','SF2']
vbs = ['SC33', 'SF2', 'SL34', 'SF3', 'SF4', 'SF5', 'SC44']
# VBS = {'SL32':int(ND_Sample_N['SL32'][ITEM]),'SC22':int(ND_Sample_N['SC22'][ITEM]),'SF1':int(ND_Sample_N['SF1'][ITEM]),
# 'SC33':int(ND_Sample_N['SC33'][ITEM]),'SF2':int(ND_Sample_N['SF2'][ITEM])}
# EDS = {'CP2B21':int(ND_Sample_N['CP2B21'][ITEM]),'CB22B21':int(ND_Sample_N['CB22B21'][ITEM]),
# 'CB32B22':int(ND_Sample_N['CB32B22'][ITEM]),'CB32B31':int(ND_Sample_N['CB32B31'][ITEM]),
# 'CP3B31':int(ND_Sample_N['CP3B31'][ITEM])}
VBS = {
'SC33': int(ND_Sample_N['SC33'][ITEM]),
'SF2': int(ND_Sample_N['SF2'][ITEM]),
'SL34': int(ND_Sample_N['SL34'][ITEM]),
'SF3': int(ND_Sample_N['SF3'][ITEM]),
'SF4': int(ND_Sample_N['SF4'][ITEM]),
'SF5': int(ND_Sample_N['SF5'][ITEM]),
'SC44': int(ND_Sample_N['SC44'][ITEM])
}
EDS = {
'CP4B41': int(ND_Sample_N['CP4B41'][ITEM]),
'CB42B41': int(ND_Sample_N['CB42B41'][ITEM]),
'CB43B42': int(ND_Sample_N['CB43B42'][ITEM]),
'CB44B43': int(ND_Sample_N['CB44B43'][ITEM]),
'CB44B32': int(ND_Sample_N['CB44B32'][ITEM]),
'CB32B31': int(ND_Sample_N['CB32B31'][ITEM]),
'CP3B32': int(ND_Sample_N['CP3B32'][ITEM])
}
query = bp_N.map_query(variables=vbs, evidence=EDS)
for key in query:
if query[key] != VBS[key]:
error += 1
print(error)
from pgmpy.factors.discrete import TabularCPD
from pgmpy.models import BayesianModel
from pgmpy.inference import BeliefPropagation
bayesian_model = BayesianModel([('A', 'J'), ('R', 'J'), ('J', 'Q'), ('J', 'L'),
('G', 'L')])
cpd_a = TabularCPD('A', 2, [[0.2], [0.8]])
cpd_r = TabularCPD('R', 2, [[0.4], [0.6]])
cpd_j = TabularCPD('J', 2, [[0.9, 0.6, 0.7, 0.1], [0.1, 0.4, 0.3, 0.9]],
['R', 'A'], [2, 2])
cpd_q = TabularCPD('Q', 2, [[0.9, 0.2], [0.1, 0.8]], ['J'], [2])
cpd_l = TabularCPD('L', 2, [[0.9, 0.45, 0.8, 0.1], [0.1, 0.55, 0.2, 0.9]],
['G', 'J'], [2, 2])
cpd_g = TabularCPD('G', 2, [[0.6], [0.4]])
bayesian_model.add_cpds(cpd_a, cpd_r, cpd_j, cpd_q, cpd_l, cpd_g)
belief_propagation = BeliefPropagation(bayesian_model)
print(
belief_propagation.map_query(variables=['J', 'Q'],
evidence={
'A': 0,
'R': 0,
'G': 0,
'L': 1
}))
test_value[i] = 0
elif (min_value[i] == 1):
if (test_value[i] < 1):
test_value[i] = 0
else:
test_value[i] = test_value[i] - 1
#print(model.check_model())
#bp.calibrate()
bp = BeliefPropagation(new_model)
result = bp.map_query(variables=['AN'],
evidence={
'CsM': test_value[0],
'CsH': test_value[1],
'CsI': test_value[2],
'InI': test_value[3],
'InD': test_value[4],
'InN': test_value[5],
'OutI': test_value[6],
'OutD': test_value[7],
'OutN': test_value[8],
'PitC': test_value[9],
'PitUp': test_value[10],
'PitD': test_value[11],
'PitT': test_value[12],
'PitUn': test_value[13],
'IDro': test_value[14],
'DDro': test_value[15],
'NDro': test_value[16],
'PitN': test_value[17]
})
print(result['AN'])
('PVS', 'VTUB'), ('PVS', 'ACO2'), ('SAO2', 'VMCH'),
('SAO2', 'VLNG'), ('SAO2', 'VALV'), ('SAO2', 'ACO2'),
('SHNT', 'INT'), ('INT', 'VALV'), ('PRSS', 'VTUB'),
('DISC', 'VTUB'), ('MVS', 'VMCH'), ('VMCH', 'VTUB'),
('VMCH', 'VALV'), ('VTUB', 'VLNG'), ('VTUB', 'VALV'),
('VLNG', 'VALV'), ('VLNG', 'ACO2'), ('VALV', 'ACO2'),
('CCHL', 'HR'), ('HR', 'CO'), ('CO', 'BP'),
('HRBP', 'INT')])
# pe = ParameterEstimator(model, df)
# print("\n", pe.state_counts('SAO2'))
mle = MaximumLikelihoodEstimator(model, df)
model.fit(df, MaximumLikelihoodEstimator)
model.fit(df, estimator=BayesianEstimator)
# infer = VariableElimination(model)
# print (" **************** Inference using variable elimination ...***********");
# print ("VALV : 'VTUB':0,'PRSS':1");
# print (infer.query(['VALV'],evidence={'VTUB':0,'PRSS':1}) ['VALV'])
print(" ****************Inference using belief propagation ...***********")
print("VALV : 'VTUB':0,'PRSS':1")
belief_propagation = BeliefPropagation(model)
belief_propagation.map_query(variables=['VALV'],
evidence={
'VTUB': 0,
'PRSS': 0
})
belief_propagation = BeliefPropagation(model)
# To calibrate the clique tree, use calibrate() method
belief_propagation.calibrate()
# To get cluster (or clique) beliefs use the corresponding getters
belief_propagation.get_clique_beliefs()
# To get the sepset beliefs use the corresponding getters
belief_propagation.get_sepset_beliefs()
>> # Query variables not in the same cluster
belief_propagation.query(variables=['no_of_people'], evidence={'location':1, 'quality':1})
>> # Can apply MAP_Query - next
belief_propagation.map_query(variables=['no_of_people'], evidence={'location':1, 'quality':1})
" {'no_of_people': 0} "
-4- " MAP - Maximize A Posterior Probability "
" Given the current states to find out the maximized predicted var state - prediction "
" Different from Query which only care find out the distribution of target var over all states "
** " MAP using Variable Elimination "
" Using factor maximization "
('Q11', 'attendance'), ('Q12', 'attendance'), ('class', 'difficulty'),
('class', 'Q7'), ('class', 'Q9'), ('difficulty', 'Q9'), ('class', 'Q11'),
('Q18', 'Q16'), ('Q13', 'Q25'), ('Q23', 'Q25'), ('class', 'Q12'),
('Q17', 'Q12')
])
amlmodel = bayesmodel.fit(df, estimator=MaximumLikelihoodEstimator)
for cpd in bayesmodel.get_cpds():
print("CPD of {variable}:".format(variable=cpd.variable))
print(cpd)
belpro = BeliefPropagation(bayesmodel)
print(
belpro.map_query(variables=['attendance'],
evidence={
'difficulty': 2,
'Q9': 3
}))
# print(belpro.map_query(variables=['Q25', 'Q18','Q16'],evidence={'instr':1}))
print(
belpro.map_query(variables=['attendance', 'Q9', 'difficulty'],
evidence={'class': 7}))
#Commented some queries because taking a lot of time to run
# print(belpro.map_query(variables=['Q28','Q11'],evidence={'instr':2, 'class':10}))
# print(belpro.map_query(variables=['Q18', 'Q26','Q13'],evidence={'instr':2}))
# print(belpro.map_query(variables=['Q23', 'Q21','Q17'],evidence={'instr':2}))
inference = BayesianModelSampling(bayesmodel)
df = inference.forward_sample(5)
def test_map_query_with_evidence(self):
belief_propagation = BeliefPropagation(self.bayesian_model)
map_query = belief_propagation.map_query(['A', 'R', 'L'],
{'J': 0, 'Q': 1, 'G': 0})
self.assertDictEqual(map_query, {'A': 1, 'R': 0, 'L': 0})
def test_map_query(self):
belief_propagation = BeliefPropagation(self.bayesian_model)
map_query = belief_propagation.map_query()
self.assertDictEqual(map_query, {'A': 1, 'R': 1, 'J': 1, 'Q': 1, 'G': 0,
'L': 0})