def setUp(self):
# A test Bayesian model
diff_cpd = TabularCPD('diff', 2, [[0.6], [0.4]])
intel_cpd = TabularCPD('intel', 2, [[0.7], [0.3]])
grade_cpd = TabularCPD('grade',
3,
[[0.3, 0.05, 0.9, 0.5], [0.4, 0.25, 0.08, 0.3],
[0.3, 0.7, 0.02, 0.2]],
evidence=['diff', 'intel'],
evidence_card=[2, 2])
self.bayesian_model = BayesianModel()
self.bayesian_model.add_nodes_from(['diff', 'intel', 'grade'])
self.bayesian_model.add_edges_from([('diff', 'grade'),
('intel', 'grade')])
self.bayesian_model.add_cpds(diff_cpd, intel_cpd, grade_cpd)
# A test Markov model
self.markov_model = MarkovModel([('A', 'B'), ('C', 'B'), ('B', 'D')])
factor_ab = DiscreteFactor(['A', 'B'], [2, 3], [1, 2, 3, 4, 5, 6])
factor_cb = DiscreteFactor(['C', 'B'], [4, 3],
[3, 1, 4, 5, 7, 8, 1, 3, 10, 4, 5, 6])
factor_bd = DiscreteFactor(['B', 'D'], [3, 2], [5, 7, 2, 1, 9, 3])
self.markov_model.add_factors(factor_ab, factor_cb, factor_bd)
self.gibbs = GibbsSampling(self.bayesian_model)
def test_check_model2(self):
cpd_s = TabularCPD('s',
2,
values=np.array([[0.5, 0.3], [0.8, 0.7]]),
evidence=['i'],
evidence_card=[2])
self.G.add_cpds(cpd_s)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(cpd_s)
cpd_g = TabularCPD('g',
2,
values=np.array([[0.2, 0.3, 0.4, 0.6],
[0.3, 0.7, 0.6, 0.4]]),
evidence=['d', 'i'],
evidence_card=[2, 2])
self.G.add_cpds(cpd_g)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(cpd_g)
cpd_l = TabularCPD('l',
2,
values=np.array([[0.2, 0.3], [0.1, 0.7]]),
evidence=['g'],
evidence_card=[2])
self.G.add_cpds(cpd_l)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(cpd_l)
def test_check_model(self):
cpd_g = TabularCPD('g',
2,
values=np.array([[0.2, 0.3, 0.4, 0.6],
[0.8, 0.7, 0.6, 0.4]]),
evidence=['d', 'i'],
evidence_card=[2, 2])
cpd_s = TabularCPD('s',
2,
values=np.array([[0.2, 0.3], [0.8, 0.7]]),
evidence=['i'],
evidence_card=[2])
cpd_l = TabularCPD('l',
2,
values=np.array([[0.2, 0.3], [0.8, 0.7]]),
evidence=['g'],
evidence_card=[2])
self.G.add_cpds(cpd_g, cpd_s, cpd_l)
self.assertRaises(ValueError, self.G.check_model)
cpd_d = TabularCPD('d', 2, values=[[0.8, 0.2]])
cpd_i = TabularCPD('i', 2, values=[[0.7, 0.3]])
self.G.add_cpds(cpd_d, cpd_i)
self.assertTrue(self.G.check_model())
def test_add_multiple_cpds(self):
cpd_d = TabularCPD('d', 2, values=np.random.rand(2, 1))
cpd_i = TabularCPD('i', 2, values=np.random.rand(2, 1))
cpd_g = TabularCPD('g',
2,
values=np.random.rand(2, 4),
evidence=['d', 'i'],
evidence_card=[2, 2])
cpd_l = TabularCPD('l',
2,
values=np.random.rand(2, 2),
evidence=['g'],
evidence_card=[2])
cpd_s = TabularCPD('s',
2,
values=np.random.rand(2, 2),
evidence=['i'],
evidence_card=[2])
self.G.add_cpds(cpd_d, cpd_i, cpd_g, cpd_l, cpd_s)
self.assertEqual(self.G.get_cpds('d'), cpd_d)
self.assertEqual(self.G.get_cpds('i'), cpd_i)
self.assertEqual(self.G.get_cpds('g'), cpd_g)
self.assertEqual(self.G.get_cpds('l'), cpd_l)
self.assertEqual(self.G.get_cpds('s'), cpd_s)
def initialize_initial_state(self):
"""
This method will automatically re-adjust the cpds and the edges added to the bayesian network.
If an edge that is added as an intra time slice edge in the 0th timeslice, this method will
automatically add it in the 1st timeslice. It will also add the cpds. However, to call this
method, one needs to add cpds as well as the edges in the bayesian network of the whole
skeleton including the 0th and the 1st timeslice,.
Examples:
-------
>>> from pgm.models import DynamicBayesianNetwork as DBN
>>> from pgm.factors.discrete import TabularCPD
>>> student = DBN()
>>> student.add_nodes_from(['D', 'G', 'I', 'S', 'L'])
>>> student.add_edges_from([(('D', 0),('G', 0)),(('I', 0),('G', 0)),(('D', 0),('D', 1)),(('I', 0),('I', 1))])
>>> grade_cpd = TabularCPD(('G', 0), 3, [[0.3, 0.05, 0.9, 0.5],
... [0.4, 0.25, 0.8, 0.03],
... [0.3, 0.7, 0.02, 0.2]],
... evidence=[('I', 0),('D', 0)],
... evidence_card=[2, 2])
>>> d_i_cpd = TabularCPD(('D', 1), 2, [[0.6, 0.3],
... [0.4, 0.7]],
... evidence=[('D', 0)],
... evidence_card=2)
>>> diff_cpd = TabularCPD(('D', 0), 2, [[0.6, 0.4]])
>>> intel_cpd = TabularCPD(('I',0), 2, [[0.7, 0.3]])
>>> i_i_cpd = TabularCPD(('I', 1), 2, [[0.5, 0.4],
... [0.5, 0.6]],
... evidence=[('I', 0)],
... evidence_card=2)
>>> student.add_cpds(grade_cpd, d_i_cpd, diff_cpd, intel_cpd, i_i_cpd)
>>> student.initialize_initial_state()
"""
for cpd in self.cpds:
temp_var = (cpd.variable[0], 1 - cpd.variable[1])
parents = self.get_parents(temp_var)
if not any(x.variable == temp_var for x in self.cpds):
if all(x[1] == parents[0][1] for x in parents):
if parents:
evidence_card = cpd.cardinality[:0:-1]
new_cpd = TabularCPD(
temp_var, cpd.variable_card,
cpd.values.reshape(cpd.variable_card,
np.prod(evidence_card)),
parents, evidence_card)
else:
if cpd.get_evidence():
initial_cpd = cpd.marginalize(cpd.get_evidence(),
inplace=False)
new_cpd = TabularCPD(
temp_var, cpd.variable_card,
np.reshape(initial_cpd.values, (-1, 2)))
else:
new_cpd = TabularCPD(
temp_var, cpd.variable_card,
np.reshape(cpd.values, (-1, 2)))
self.add_cpds(new_cpd)
self.check_model()
def phm_bin(self, fname, cpd_w, phm_dat):
weakness = 'Weakness %s' % fname
detection = 'Detection %s' % fname
corrective_action = 'Corrective Action %s' % fname
failure = 'Failure %s' % fname
if phm_dat['On'][fname]:
self.model.add_edges_from([(weakness, detection),
(weakness, corrective_action),
(weakness, failure),
(detection, corrective_action),
(corrective_action, failure)])
# Defining individual PHMs data.
phm_eff = phm_dat['Efficiency'][fname]['zero']
phm_err = phm_dat['Error'][fname]
phm_fck = phm_dat['Fuckup'][fname]
phm_rpr = phm_dat['Repair'][fname]['zero']
cpd_d = TabularCPD(variable=detection,
variable_card=2,
values=[[phm_eff, phm_err],
[(1 - phm_eff), (1 - phm_err)]],
evidence=[weakness],
evidence_card=[2])
# 0 or 1, action or not. This can be more nuanced, teams available, expertise, training, management, etc.
# A detection might not automatically generate a corrective action.
cpd_c = TabularCPD(variable=corrective_action,
variable_card=2,
values=[[1., 0., 1., 0.], [0., 1., 0., 1.]],
evidence=[weakness, detection],
evidence_card=[2, 2])
cpd_f = TabularCPD(variable=failure,
variable_card=2,
values=[[1 - phm_rpr, 1., phm_fck, 0.],
[phm_rpr, 0., 1 - phm_fck, 1.]],
evidence=[weakness, corrective_action],
evidence_card=[2, 2])
# Associating the CPDs with the network
self.model.add_cpds(cpd_w, cpd_d, cpd_c, cpd_f)
else:
self.model.add_edges_from([(weakness, failure)])
cpd_f = TabularCPD(variable=failure,
variable_card=2,
values=[[1., 0.], [0., 1.]],
evidence=[weakness],
evidence_card=[2])
self.model.add_cpds(cpd_w, cpd_f)
# check_model checks for the network structure and CPDs and verifies that the CPDs are correctly
# defined and sum to 1.
self.model.check_model()
return self.model
def estimate_cpd(self, node):
"""
Method to estimate the CPD for a given variable.
Parameters
----------
node: int, string (any hashable python object)
The name of the variable for which the CPD is to be estimated.
Returns
-------
CPD: TabularCPD
Examples
--------
>>> import pandas as pd
>>> from pgm.models import BayesianModel
>>> from pgm.estimators import MaximumLikelihoodEstimator
>>> data = pd.DataFrame(data={'A': [0, 0, 1], 'B': [0, 1, 0], 'C': [1, 1, 0]})
>>> model = BayesianModel([('A', 'C'), ('B', 'C')])
>>> cpd_A = MaximumLikelihoodEstimator(model, data).estimate_cpd('A')
>>> print(cpd_A)
╒══════╤══════════╕
│ A(0) │ 0.666667 │
├──────┼──────────┤
│ A(1) │ 0.333333 │
╘══════╧══════════╛
>>> cpd_C = MaximumLikelihoodEstimator(model, data).estimate_cpd('C')
>>> print(cpd_C)
╒══════╤══════╤══════╤══════╤══════╕
│ A │ A(0) │ A(0) │ A(1) │ A(1) │
├──────┼──────┼──────┼──────┼──────┤
│ B │ B(0) │ B(1) │ B(0) │ B(1) │
├──────┼──────┼──────┼──────┼──────┤
│ C(0) │ 0.0 │ 0.0 │ 1.0 │ 0.5 │
├──────┼──────┼──────┼──────┼──────┤
│ C(1) │ 1.0 │ 1.0 │ 0.0 │ 0.5 │
╘══════╧══════╧══════╧══════╧══════╛
"""
state_counts = self.state_counts(node)
# if a column contains only `0`s (no states observed for some configuration
# of parents' states) fill that column uniformly instead
state_counts.ix[:, (state_counts == 0).all()] = 1
parents = sorted(self.model.get_parents(node))
parents_cardinalities = [len(self.state_names[parent]) for parent in parents]
node_cardinality = len(self.state_names[node])
cpd = TabularCPD(node, node_cardinality, np.array(state_counts),
evidence=parents,
evidence_card=parents_cardinalities,
state_names=self.state_names)
cpd.normalize()
return cpd
def test_get_cpd_raises_error(self):
cpd1 = TabularCPD('diff', 2, values=np.random.rand(2, 1))
cpd2 = TabularCPD('intel', 2, values=np.random.rand(2, 1))
cpd3 = TabularCPD('grade',
2,
values=np.random.rand(2, 4),
evidence=['diff', 'intel'],
evidence_card=[2, 2])
self.graph.add_edges_from([('diff', 'grade'), ('intel', 'grade')])
self.graph.add_cpds(cpd1, cpd2, cpd3)
self.assertRaises(ValueError, self.graph.get_cpds, 'sat')
def test_estimate_cpd_dirichlet(self):
cpd_A = self.est1.estimate_cpd('A', prior_type="dirichlet", pseudo_counts=[0, 1])
self.assertEqual(cpd_A, TabularCPD('A', 2, [[0.5], [0.5]]))
cpd_B = self.est1.estimate_cpd('B', prior_type="dirichlet", pseudo_counts=[9, 3])
self.assertEqual(cpd_B, TabularCPD('B', 2, [[11.0/15], [4.0/15]]))
cpd_C = self.est1.estimate_cpd('C', prior_type="dirichlet", pseudo_counts=[0.4, 0.6])
self.assertEqual(cpd_C, TabularCPD('C', 2, [[0.2, 0.2, 0.7, 0.4],
[0.8, 0.8, 0.3, 0.6]],
evidence=['A', 'B'], evidence_card=[2, 2]))
def test_add_multiple_cpds(self):
cpd1 = TabularCPD('diff', 2, values=np.random.rand(2, 1))
cpd2 = TabularCPD('intel', 2, values=np.random.rand(2, 1))
cpd3 = TabularCPD('grade',
2,
values=np.random.rand(2, 4),
evidence=['diff', 'intel'],
evidence_card=[2, 2])
self.graph.add_edges_from([('diff', 'grade'), ('intel', 'grade')])
self.graph.add_cpds(cpd1, cpd2, cpd3)
self.assertListEqual(self.graph.get_cpds(), [cpd1, cpd2, cpd3])
def test_get_cpd_for_node(self):
cpd1 = TabularCPD('diff', 2, values=np.random.rand(2, 1))
cpd2 = TabularCPD('intel', 2, values=np.random.rand(2, 1))
cpd3 = TabularCPD('grade',
2,
values=np.random.rand(2, 4),
evidence=['diff', 'intel'],
evidence_card=[2, 2])
self.graph.add_edges_from([('diff', 'grade'), ('intel', 'grade')])
self.graph.add_cpds(cpd1, cpd2, cpd3)
self.assertEqual(self.graph.get_cpds('diff'), cpd1)
self.assertEqual(self.graph.get_cpds('intel'), cpd2)
self.assertEqual(self.graph.get_cpds('grade'), cpd3)
def test_fit_missing_data(self):
self.model2.fit(self.data2,
state_names={'C': [0, 1]},
complete_samples_only=False)
cpds = set([
TabularCPD('A', 2, [[0.5], [0.5]]),
TabularCPD('B', 2, [[2. / 3], [1. / 3]]),
TabularCPD('C',
2, [[0, 0.5, 0.5, 0.5], [1, 0.5, 0.5, 0.5]],
evidence=['A', 'B'],
evidence_card=[2, 2])
])
self.assertSetEqual(cpds, set(self.model2.get_cpds()))
def test_estimate_cpd_shortcuts(self):
cpd_C1 = self.est2.estimate_cpd('C', prior_type='BDeu', equivalent_sample_size=9)
cpd_C1_correct = TabularCPD('C', 3, [[0.2, 0.2, 0.6, 1./3, 1./3, 1./3],
[0.6, 0.6, 0.2, 1./3, 1./3, 1./3],
[0.2, 0.2, 0.2, 1./3, 1./3, 1./3]],
evidence=['A', 'B'], evidence_card=[3, 2])
self.assertEqual(cpd_C1, cpd_C1_correct)
cpd_C2 = self.est3.estimate_cpd('C', prior_type='K2')
cpd_C2_correct = TabularCPD('C', 2, [[0.5, 0.6, 1./3, 2./3, 0.75, 2./3],
[0.5, 0.4, 2./3, 1./3, 0.25, 1./3]],
evidence=['A', 'B'], evidence_card=[3, 2])
self.assertEqual(cpd_C2, cpd_C2_correct)
def setUp(self):
self.G = BayesianModel([('a', 'd'), ('b', 'd'), ('d', 'e'),
('b', 'c')])
self.G1 = BayesianModel([('diff', 'grade'), ('intel', 'grade')])
diff_cpd = TabularCPD('diff', 2, values=[[0.2], [0.8]])
intel_cpd = TabularCPD('intel', 3, values=[[0.5], [0.3], [0.2]])
grade_cpd = TabularCPD('grade',
3,
values=[[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
[0.8, 0.8, 0.8, 0.8, 0.8, 0.8]],
evidence=['diff', 'intel'],
evidence_card=[2, 3])
self.G1.add_cpds(diff_cpd, intel_cpd, grade_cpd)
def setUp(self):
dbn_1 = DynamicBayesianNetwork()
dbn_1.add_edges_from(
[(('Z', 0), ('X', 0)), (('Z', 0), ('Y', 0)), (('Z', 0), ('Z', 1))])
cpd_start_z_1 = TabularCPD(('Z', 0), 2, [[0.8, 0.2]])
cpd_x_1 = TabularCPD(
('X', 0), 2, [[0.9, 0.6], [0.1, 0.4]], [('Z', 0)], [2])
cpd_y_1 = TabularCPD(
('Y', 0), 2, [[0.7, 0.2], [0.3, 0.8]], [('Z', 0)], [2])
cpd_trans_z_1 = TabularCPD(
('Z', 1), 2, [[0.9, 0.1], [0.1, 0.9]], [('Z', 0)], [2])
dbn_1.add_cpds(cpd_start_z_1, cpd_trans_z_1, cpd_x_1, cpd_y_1)
dbn_1.initialize_initial_state()
self.dbn_inference_1 = DBNInference(dbn_1)
dbn_2 = DynamicBayesianNetwork()
dbn_2.add_edges_from([(('Z', 0), ('X', 0)), (('X', 0), ('Y', 0)),
(('Z', 0), ('Z', 1))])
cpd_start_z_2 = TabularCPD(('Z', 0), 2, [[0.5, 0.5]])
cpd_x_2 = TabularCPD(
('X', 0), 2, [[0.6, 0.9], [0.4, 0.1]], [('Z', 0)], [2])
cpd_y_2 = TabularCPD(
('Y', 0), 2, [[0.2, 0.3], [0.8, 0.7]], [('X', 0)], [2])
cpd_z_2 = TabularCPD(
('Z', 1), 2, [[0.4, 0.7], [0.6, 0.3]], [('Z', 0)], [2])
dbn_2.add_cpds(cpd_x_2, cpd_y_2, cpd_z_2, cpd_start_z_2)
dbn_2.initialize_initial_state()
self.dbn_inference_2 = DBNInference(dbn_2)
def test_get_cpds1(self):
self.model = BayesianModel([('A', 'AB')])
cpd_a = TabularCPD('A', 2, values=np.random.rand(2, 1))
cpd_ab = TabularCPD('AB',
2,
values=np.random.rand(2, 2),
evidence=['A'],
evidence_card=[2])
self.model.add_cpds(cpd_a, cpd_ab)
self.assertEqual(self.model.get_cpds('A').variable, 'A')
self.assertEqual(self.model.get_cpds('AB').variable, 'AB')
self.assertRaises(ValueError, self.model.get_cpds, 'B')
self.model.add_node('B')
self.assertRaises(ValueError, self.model.get_cpds, 'B')
def setUp(self):
edges = [['family-out', 'dog-out'], ['bowel-problem', 'dog-out'],
['family-out', 'light-on'], ['dog-out', 'hear-bark']]
cpds = {
'bowel-problem': np.array([[0.01], [0.99]]),
'dog-out': np.array([[0.99, 0.01, 0.97, 0.03],
[0.9, 0.1, 0.3, 0.7]]),
'family-out': np.array([[0.15], [0.85]]),
'hear-bark': np.array([[0.7, 0.3], [0.01, 0.99]]),
'light-on': np.array([[0.6, 0.4], [0.05, 0.95]])
}
states = {
'bowel-problem': ['true', 'false'],
'dog-out': ['true', 'false'],
'family-out': ['true', 'false'],
'hear-bark': ['true', 'false'],
'light-on': ['true', 'false']
}
parents = {
'bowel-problem': [],
'dog-out': ['family-out', 'bowel-problem'],
'family-out': [],
'hear-bark': ['dog-out'],
'light-on': ['family-out']
}
properties = {
'bowel-problem': ['position = (335, 99)'],
'dog-out': ['position = (300, 195)'],
'family-out': ['position = (257, 99)'],
'hear-bark': ['position = (296, 268)'],
'light-on': ['position = (218, 195)']
}
self.model = BayesianModel(edges)
tabular_cpds = []
for var in sorted(cpds.keys()):
values = cpds[var]
cpd = TabularCPD(var,
len(states[var]),
values,
evidence=parents[var],
evidence_card=[
len(states[evidence_var])
for evidence_var in parents[var]
])
tabular_cpds.append(cpd)
self.model.add_cpds(*tabular_cpds)
for node, properties in properties.items():
for prop in properties:
prop_name, prop_value = map(lambda t: t.strip(),
prop.split('='))
self.model.node[node][prop_name] = prop_value
self.writer = BIFWriter(model=self.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():
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.node[node][prop_name] = prop_value
return model
def test_add_cpds(self):
self.model.add_cpds(self.cpd1)
cpd = self.model.get_cpds('x1')
self.assertEqual(cpd.variable, self.cpd1.variable)
self.assertEqual(cpd.variance, self.cpd1.variance)
self.assertEqual(cpd.beta_0, self.cpd1.beta_0)
self.model.add_cpds(self.cpd2)
cpd = self.model.get_cpds('x2')
self.assertEqual(cpd.variable, self.cpd2.variable)
self.assertEqual(cpd.variance, self.cpd2.variance)
self.assertEqual(cpd.beta_0, self.cpd2.beta_0)
self.assertEqual(cpd.evidence, self.cpd2.evidence)
np_test.assert_array_equal(cpd.beta_vector, self.cpd2.beta_vector)
self.model.add_cpds(self.cpd3)
cpd = self.model.get_cpds('x3')
self.assertEqual(cpd.variable, self.cpd3.variable)
self.assertEqual(cpd.variance, self.cpd3.variance)
self.assertEqual(cpd.beta_0, self.cpd3.beta_0)
self.assertEqual(cpd.evidence, self.cpd3.evidence)
np_test.assert_array_equal(cpd.beta_vector, self.cpd3.beta_vector)
tab_cpd = TabularCPD(
'grade',
3, [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
[0.8, 0.8, 0.8, 0.8, 0.8, 0.8]],
evidence=['diff', 'intel'],
evidence_card=[2, 3])
self.assertRaises(ValueError, self.model.add_cpds, tab_cpd)
self.assertRaises(ValueError, self.model.add_cpds, 1)
self.assertRaises(ValueError, self.model.add_cpds, 1, tab_cpd)
def test_estimate_cpd_improper_prior(self):
cpd_C = self.est1.estimate_cpd('C', prior_type="dirichlet", pseudo_counts=[0, 0])
cpd_C_correct = (TabularCPD('C', 2, [[0.0, 0.0, 1.0, np.NaN],
[1.0, 1.0, 0.0, np.NaN]],
evidence=['A', 'B'], evidence_card=[2, 2],
state_names={'A': [0, 1], 'B': [0, 1], 'C': [0, 1]}))
# manual comparison because np.NaN != np.NaN
self.assertTrue(((cpd_C.values == cpd_C_correct.values) |
np.isnan(cpd_C.values) & np.isnan(cpd_C_correct.values)).all())
def test_bayesian_fit(self):
print(isinstance(BayesianEstimator, BaseEstimator))
print(isinstance(MaximumLikelihoodEstimator, BaseEstimator))
self.model2.fit(self.data1,
estimator_type=BayesianEstimator,
prior_type="dirichlet",
pseudo_counts=[9, 3])
self.assertEqual(self.model2.get_cpds('B'),
TabularCPD('B', 2, [[11.0 / 15], [4.0 / 15]]))
def test_add_single_cpd(self):
cpd = TabularCPD('grade',
2,
values=np.random.rand(2, 4),
evidence=['diff', 'intel'],
evidence_card=[2, 2])
self.graph.add_edges_from([('diff', 'grade'), ('intel', 'grade')])
self.graph.add_cpds(cpd)
self.assertListEqual(self.graph.get_cpds(), [cpd])
def setUp(self):
self.maxDiff = None
edges = [['family-out', 'dog-out'],
['bowel-problem', 'dog-out'],
['family-out', 'light-on'],
['dog-out', 'hear-bark']]
cpds = {'bowel-problem': np.array([[0.01],
[0.99]]),
'dog-out': np.array([[0.99, 0.01, 0.97, 0.03],
[0.9, 0.1, 0.3, 0.7]]),
'family-out': np.array([[0.15],
[0.85]]),
'hear-bark': np.array([[0.7, 0.3],
[0.01, 0.99]]),
'light-on': np.array([[0.6, 0.4],
[0.05, 0.95]])}
states = {'bowel-problem': ['true', 'false'],
'dog-out': ['true', 'false'],
'family-out': ['true', 'false'],
'hear-bark': ['true', 'false'],
'light-on': ['true', 'false']}
parents = {'bowel-problem': [],
'dog-out': ['bowel-problem', 'family-out'],
'family-out': [],
'hear-bark': ['dog-out'],
'light-on': ['family-out']}
self.bayesmodel = BayesianModel(edges)
tabular_cpds = []
for var, values in cpds.items():
cpd = TabularCPD(var, len(states[var]), values,
evidence=parents[var],
evidence_card=[len(states[evidence_var])
for evidence_var in parents[var]])
tabular_cpds.append(cpd)
self.bayesmodel.add_cpds(*tabular_cpds)
self.bayeswriter = UAIWriter(self.bayesmodel)
edges = {('var_0', 'var_1'), ('var_0', 'var_2'), ('var_1', 'var_2')}
self.markovmodel = MarkovModel(edges)
tables = [(['var_0', 'var_1'],
['4.000', '2.400', '1.000', '0.000']),
(['var_0', 'var_1', 'var_2'],
['2.2500', '3.2500', '3.7500', '0.0000', '0.0000', '10.0000',
'1.8750', '4.0000', '3.3330', '2.0000', '2.0000', '3.4000'])]
domain = {'var_1': '2', 'var_2': '3', 'var_0': '2'}
factors = []
for table in tables:
variables = table[0]
cardinality = [int(domain[var]) for var in variables]
values = list(map(float, table[1]))
factor = DiscreteFactor(variables, cardinality, values)
factors.append(factor)
self.markovmodel.add_factors(*factors)
self.markovwriter = UAIWriter(self.markovmodel)
def setUp(self):
self.bayesian = BayesianModel([('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'e')])
a_cpd = TabularCPD('a', 2, [[0.4, 0.6]])
b_cpd = TabularCPD('b', 2, [[0.2, 0.4], [0.8, 0.6]], evidence=['a'],
evidence_card=[2])
c_cpd = TabularCPD('c', 2, [[0.1, 0.2], [0.9, 0.8]], evidence=['b'],
evidence_card=[2])
d_cpd = TabularCPD('d', 2, [[0.4, 0.3], [0.6, 0.7]], evidence=['c'],
evidence_card=[2])
e_cpd = TabularCPD('e', 2, [[0.3, 0.2], [0.7, 0.8]], evidence=['d'],
evidence_card=[2])
self.bayesian.add_cpds(a_cpd, b_cpd, c_cpd, d_cpd, e_cpd)
self.markov = MarkovModel([('a', 'b'), ('b', 'd'), ('a', 'c'), ('c', 'd')])
factor_1 = DiscreteFactor(['a', 'b'], [2, 2], np.array([100, 1, 1, 100]))
factor_2 = DiscreteFactor(['a', 'c'], [2, 2], np.array([40, 30, 100, 20]))
factor_3 = DiscreteFactor(['b', 'd'], [2, 2], np.array([1, 100, 100, 1]))
factor_4 = DiscreteFactor(['c', 'd'], [2, 2], np.array([60, 60, 40, 40]))
self.markov.add_factors(factor_1, factor_2, factor_3, factor_4)
def setUp(self):
self.network = DynamicBayesianNetwork()
self.grade_cpd = TabularCPD(('G', 0),
3,
values=[[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[('D', 0), ('I', 0)],
evidence_card=[2, 2])
self.d_i_cpd = TabularCPD(('D', 1),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[('D', 0)],
evidence_card=[2])
self.diff_cpd = TabularCPD(('D', 0), 2, values=[[0.6, 0.4]])
self.intel_cpd = TabularCPD(('I', 0), 2, values=[[0.7, 0.3]])
self.i_i_cpd = TabularCPD(('I', 1),
2,
values=[[0.5, 0.4], [0.5, 0.6]],
evidence=[('I', 0)],
evidence_card=[2])
self.grade_1_cpd = TabularCPD(('G', 1),
3,
values=[[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[('D', 1), ('I', 1)],
evidence_card=[2, 2])
def setUp(self):
self.junction_tree = JunctionTree([(('A', 'B'), ('B', 'C')),
(('B', 'C'), ('C', 'D'))])
phi1 = DiscreteFactor(['A', 'B'], [2, 3], range(6))
phi2 = DiscreteFactor(['B', 'C'], [3, 2], range(6))
phi3 = DiscreteFactor(['C', 'D'], [2, 2], range(4))
self.junction_tree.add_factors(phi1, phi2, phi3)
self.bayesian_model = BayesianModel([('A', 'J'), ('R', 'J'),
('J', 'Q'), ('J', 'L'),
('G', 'L')])
cpd_a = TabularCPD('A', 2, values=[[0.2], [0.8]])
cpd_r = TabularCPD('R', 2, values=[[0.4], [0.6]])
cpd_j = TabularCPD('J',
2,
values=[[0.9, 0.6, 0.7, 0.1], [0.1, 0.4, 0.3, 0.9]],
evidence=['A', 'R'],
evidence_card=[2, 2])
cpd_q = TabularCPD('Q',
2,
values=[[0.9, 0.2], [0.1, 0.8]],
evidence=['J'],
evidence_card=[2])
cpd_l = TabularCPD('L',
2,
values=[[0.9, 0.45, 0.8, 0.1],
[0.1, 0.55, 0.2, 0.9]],
evidence=['J', 'G'],
evidence_card=[2, 2])
cpd_g = TabularCPD('G', 2, values=[[0.6], [0.4]])
self.bayesian_model.add_cpds(cpd_a, cpd_g, cpd_j, cpd_l, cpd_q, cpd_r)
def setUp(self):
# It is just a moralised version of the above Bayesian network so all the results are same. Only factors
# are under consideration for inference so this should be fine.
self.markov_model = MarkovModel([('A', 'J'), ('R', 'J'), ('J', 'Q'),
('J', 'L'), ('G', 'L'), ('A', 'R'),
('J', 'G')])
factor_a = TabularCPD('A', 2, values=[[0.2], [0.8]]).to_factor()
factor_r = TabularCPD('R', 2, values=[[0.4], [0.6]]).to_factor()
factor_j = TabularCPD('J',
2,
values=[[0.9, 0.6, 0.7, 0.1],
[0.1, 0.4, 0.3, 0.9]],
evidence=['A', 'R'],
evidence_card=[2, 2]).to_factor()
factor_q = TabularCPD('Q',
2,
values=[[0.9, 0.2], [0.1, 0.8]],
evidence=['J'],
evidence_card=[2]).to_factor()
factor_l = TabularCPD('L',
2,
values=[[0.9, 0.45, 0.8, 0.1],
[0.1, 0.55, 0.2, 0.9]],
evidence=['J', 'G'],
evidence_card=[2, 2]).to_factor()
factor_g = TabularCPD('G', 2, [[0.6], [0.4]]).to_factor()
self.markov_model.add_factors(factor_a, factor_r, factor_j, factor_q,
factor_l, factor_g)
self.markov_inference = VariableElimination(self.markov_model)
def setUp(self):
self.bayesian_model = BayesianModel([('A', 'J'), ('R', 'J'),
('J', 'Q'), ('J', 'L'),
('G', 'L')])
cpd_a = TabularCPD('A', 2, values=[[0.2], [0.8]])
cpd_r = TabularCPD('R', 2, values=[[0.4], [0.6]])
cpd_j = TabularCPD('J',
2,
values=[[0.9, 0.6, 0.7, 0.1], [0.1, 0.4, 0.3, 0.9]],
evidence=['A', 'R'],
evidence_card=[2, 2])
cpd_q = TabularCPD('Q',
2,
values=[[0.9, 0.2], [0.1, 0.8]],
evidence=['J'],
evidence_card=[2])
cpd_l = TabularCPD('L',
2,
values=[[0.9, 0.45, 0.8, 0.1],
[0.1, 0.55, 0.2, 0.9]],
evidence=['J', 'G'],
evidence_card=[2, 2])
cpd_g = TabularCPD('G', 2, values=[[0.6], [0.4]])
self.bayesian_model.add_cpds(cpd_a, cpd_g, cpd_j, cpd_l, cpd_q, cpd_r)
self.bayesian_inference = VariableElimination(self.bayesian_model)
def test_copy(self):
self.network.add_edges_from([
(('D', 0), ('G', 0)), (('I', 0), ('G', 0)), (('D', 0), ('D', 1)),
(('I', 0), ('I', 1))
])
cpd = TabularCPD(('G', 0),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[('D', 0), ('I', 0)],
evidence_card=[2, 2])
self.network.add_cpds(cpd)
copy = self.network.copy()
self.assertIsInstance(copy, DynamicBayesianNetwork)
self.assertListEqual(sorted(self.network.nodes()),
sorted(copy.nodes()))
self.assertListEqual(sorted(self.network.edges()),
sorted(copy.edges()))
self.assertListEqual(self.network.get_cpds(), copy.get_cpds())
self.assertListEqual(sorted(self.network.get_intra_edges()),
sorted(copy.get_intra_edges()))
self.assertListEqual(sorted(self.network.get_inter_edges()),
sorted(copy.get_inter_edges()))
self.assertListEqual(sorted(self.network.get_slice_nodes()),
sorted(copy.get_slice_nodes()))
copy.cpds[0].values = np.array([[0.4, 0.05, 0.3, 0.5],
[0.3, 0.25, 0.5, 0.3],
[0.3, 0.7, 0.2, 0.2]])
self.assertNotEqual(self.network.get_cpds(), copy.get_cpds())
self.network.add_cpds(self.i_i_cpd, self.d_i_cpd)
copy.add_cpds(self.diff_cpd, self.intel_cpd)
self.network.add_node('A')
copy.add_node('Z')
self.network.add_edge(('A', 0), ('D', 0))
copy.add_edge(('Z', 0), ('D', 0))
self.assertNotEqual(sorted(self.network.nodes()), sorted(copy.nodes()))
self.assertNotEqual(sorted(self.network.edges()), sorted(copy.edges()))
self.assertNotEqual(self.network.get_cpds(), copy.get_cpds())
self.assertNotEqual(sorted(self.network.get_intra_edges()),
sorted(copy.get_intra_edges()))
self.assertListEqual(sorted(self.network.get_inter_edges()),
sorted(copy.get_inter_edges()))
self.assertNotEqual(sorted(self.network.get_slice_nodes()),
sorted(copy.get_slice_nodes()))
self.network.add_edge(('A', 0), ('D', 1))
copy.add_edge(('Z', 0), ('D', 1))
self.assertNotEqual(sorted(self.network.get_inter_edges()),
sorted(copy.get_inter_edges()))
def get_model(self):
"""
Returns the model instance of the ProbModel.
Return
---------------
model: an instance of BayesianModel.
Examples
-------
>>> reader = ProbModelXMLReader()
>>> reader.get_model()
"""
if self.probnet.get('type') == "BayesianNetwork":
model = BayesianModel(self.probnet['edges'].keys())
tabular_cpds = []
cpds = self.probnet['Potentials']
for cpd in cpds:
var = list(cpd['Variables'].keys())[0]
states = self.probnet['Variables'][var]['States']
evidence = cpd['Variables'][var]
evidence_card = [
len(self.probnet['Variables'][evidence_var]['States'])
for evidence_var in evidence
]
arr = list(map(float, cpd['Values'].split()))
values = np.array(arr)
values = values.reshape(
(len(states), values.size // len(states)))
tabular_cpds.append(
TabularCPD(var, len(states), values, evidence,
evidence_card))
model.add_cpds(*tabular_cpds)
variables = model.nodes()
for var in variables:
for prop_name, prop_value in self.probnet['Variables'][
var].items():
model.node[var][prop_name] = prop_value
edges = model.edges()
for edge in edges:
for prop_name, prop_value in self.probnet['edges'][edge].items(
):
model.edge[edge[0]][edge[1]][prop_name] = prop_value
return model
else:
raise ValueError("Please specify only Bayesian Network.")
