def get_model(self):
model = BayesianModel()
model.add_nodes_from(self.variables)
model.add_edges_from(self.edge_list)
model.name = self.network_name
tabular_cpds = []
for var, values in self.variable_CPD.items():
evidence_card = [
len(self.variable_states[evidence_var])
for evidence_var in self.variable_parents[var]
]
cpd = TabularCPD(
var,
len(self.variable_states[var]),
values,
evidence=self.variable_parents[var],
evidence_card=evidence_card,
state_names=self.get_states(),
)
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for node, properties in self.variable_property.items():
for prop in properties:
if prop is not None:
prop_name, prop_value = map(lambda t: t.strip(), prop.split("="))
model.nodes[node][prop_name] = prop_value
return model
def get_model(self):
"""
Returns the fitted bayesian model
Example
----------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_model()
<pgmpy.models.BayesianModel.BayesianModel object at 0x7f20af154320>
"""
try:
model = BayesianModel(self.variable_edges)
model.name = self.network_name
model.add_nodes_from(self.variable_names)
tabular_cpds = []
for var in sorted(self.variable_cpds.keys()):
values = self.variable_cpds[var]
cpd = TabularCPD(var, len(self.variable_states[var]), values,
evidence=self.variable_parents[var],
evidence_card=[len(self.variable_states[evidence_var])
for evidence_var in self.variable_parents[var]])
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for node, properties in self.variable_properties.items():
for prop in properties:
prop_name, prop_value = map(lambda t: t.strip(), prop.split('='))
model.node[node][prop_name] = prop_value
return model
except AttributeError:
raise AttributeError('First get states of variables, edges, parents and network name')
def get_model(self):
"""
Returns an instance of Bayesian Model.
"""
model = BayesianModel(self.edges)
model.name = self.model_name
tabular_cpds = []
for var, values in self.variable_CPD.items():
evidence = values['CONDSET'] if 'CONDSET' in values else []
cpd = values['DPIS']
evidence_card = values[
'CARDINALITY'] if 'CARDINALITY' in values else []
states = self.variables[var]['STATES']
cpd = TabularCPD(var,
len(states),
cpd,
evidence=evidence,
evidence_card=evidence_card)
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for var, properties in self.variables.items():
model.node[var] = properties
return model
def get_model(self):
"""
Returns an instance of Bayesian Model.
"""
model = BayesianModel()
model.add_nodes_from(self.variables)
model.add_edges_from(self.edges)
model.name = self.model_name
tabular_cpds = []
for var, values in self.variable_CPD.items():
evidence = values["CONDSET"] if "CONDSET" in values else []
cpd = values["DPIS"]
evidence_card = values[
"CARDINALITY"] if "CARDINALITY" in values else []
states = self.variables[var]["STATES"]
cpd = TabularCPD(var,
len(states),
cpd,
evidence=evidence,
evidence_card=evidence_card)
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
if nx.__version__.startswith("1"):
for var, properties in self.variables.items():
model.nodes[var] = properties
else:
for var, properties in self.variables.items():
model._node[var] = properties
return model
def get_model(self):
model = BayesianModel(self.get_edges())
model.name = self.network_name
tabular_cpds = []
for var, values in self.variable_CPD.items():
cpd = TabularCPD(var,
len(self.variable_states[var]),
values,
evidence=self.variable_parents[var],
evidence_card=[
len(self.variable_states[evidence_var])
for evidence_var in self.variable_parents[var]
])
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for node, properties in self.variable_property.items():
for prop in properties:
prop_name, prop_value = map(lambda t: t.strip(),
prop.split('='))
model.node[node][prop_name] = prop_value
return model
def bif2bayesian(pathname, verbose=3):
"""
Returns the fitted bayesian model
Example
----------
>>> from pgmpy.readwrite import BIFReader
>>> reader = BIFReader("bif_test.bif")
>>> reader.get_model()
<pgmpy.models.BayesianModel.BayesianModel object at 0x7f20af154320>
"""
if verbose >= 3: print('[BNLEARN] Loading bif file <%s>' % (pathname))
bifmodel = readwrite.BIF.BIFReader(path=pathname)
#bifmodel.get_edges()
try:
model = BayesianModel(bifmodel.variable_edges)
model.name = bifmodel.network_name
model.add_nodes_from(bifmodel.variable_names)
tabular_cpds = []
for var in sorted(bifmodel.variable_cpds.keys()):
values = bifmodel.variable_cpds[var]
cpd = TabularCPD(
var,
len(bifmodel.variable_states[var]),
values,
evidence=bifmodel.variable_parents[var],
evidence_card=[
len(bifmodel.variable_states[evidence_var])
for evidence_var in bifmodel.variable_parents[var]
])
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
# for node, properties in bifmodel.variable_properties.items():
# for prop in properties:
# prop_name, prop_value = map(lambda t: t.strip(), prop.split('='))
# model.node[node][prop_name] = prop_value
return model
except AttributeError:
raise AttributeError(
'[BNLEARN] First get states of variables, edges, parents and network names'
)
def get_model(self):
model = BayesianModel(self.get_edges())
model.name = self.network_name
tabular_cpds = []
for var, values in self.variable_CPD.items():
cpd = TabularCPD(var, len(self.variable_states[var]), values,
evidence=self.variable_parents[var],
evidence_card=[len(self.variable_states[evidence_var])
for evidence_var in self.variable_parents[var]])
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for node, properties in self.variable_property.items():
for prop in properties:
prop_name, prop_value = map(lambda t: t.strip(), prop.split('='))
model.node[node][prop_name] = prop_value
return model
def get_model(self):
"""
Returns an instance of Bayesian Model.
"""
model = BayesianModel(self.edges)
model.name = self.model_name
tabular_cpds = []
for var, values in self.variable_CPD.items():
evidence = values['CONDSET'] if 'CONDSET' in values else []
cpd = values['DPIS']
evidence_card = values['CARDINALITY'] if 'CARDINALITY' in values else []
states = self.variables[var]['STATES']
cpd = TabularCPD(var, len(states), cpd,
evidence=evidence,
evidence_card=evidence_card)
tabular_cpds.append(cpd)
model.add_cpds(*tabular_cpds)
for var, properties in self.variables.items():
model.node[var] = properties
return model
