def setUp(self):
reader = XMLBIFReader(string=TEST_FILE)
self.expected_model = reader.get_model()
self.writer = XMLBIFWriter(self.expected_model)
self.model_stateless = BayesianModel([('D', 'G'), ('I', 'G'), ('G', 'L'), ('I', 'S')])
self.cpd_d = TabularCPD(variable='D', variable_card=2, values=[[0.6, 0.4]])
self.cpd_i = TabularCPD(variable='I', variable_card=2, values=[[0.7, 0.3]])
self.cpd_g = TabularCPD(variable='G', variable_card=3,
values=[[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=['I', 'D'],
evidence_card=[2, 2])
self.cpd_l = TabularCPD(variable='L', variable_card=2,
values=[[0.1, 0.4, 0.99],
[0.9, 0.6, 0.01]],
evidence=['G'],
evidence_card=[3])
self.cpd_s = TabularCPD(variable='S', variable_card=2,
values=[[0.95, 0.2],
[0.05, 0.8]],
evidence=['I'],
evidence_card=[2])
self.model_stateless.add_cpds(self.cpd_d, self.cpd_i, self.cpd_g, self.cpd_l, self.cpd_s)
self.writer_stateless = XMLBIFWriter(self.model_stateless)
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 = (190, 69)'],
'dog-out': ['position = (155, 165)'],
'family-out': ['position = (112, 69)'],
'hear-bark': ['position = (154, 241)'],
'light-on': ['position = (73, 165)']
}
self.model = 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.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 = XMLBIFWriter(model=self.model)
def setUp(self):
reader = XMLBIFReader(string=TEST_FILE)
self.expected_model = reader.get_model()
self.writer = XMLBIFWriter(self.expected_model)
self.model_stateless = BayesianModel([("D", "G"), ("I", "G"),
("G", "L"), ("I", "S")])
self.cpd_d = TabularCPD(variable="D",
variable_card=2,
values=[[0.6, 0.4]])
self.cpd_i = TabularCPD(variable="I",
variable_card=2,
values=[[0.7, 0.3]])
self.cpd_g = TabularCPD(
variable="G",
variable_card=3,
values=[
[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=["I", "D"],
evidence_card=[2, 2],
)
self.cpd_l = TabularCPD(
variable="L",
variable_card=2,
values=[[0.1, 0.4, 0.99], [0.9, 0.6, 0.01]],
evidence=["G"],
evidence_card=[3],
)
self.cpd_s = TabularCPD(
variable="S",
variable_card=2,
values=[[0.95, 0.2], [0.05, 0.8]],
evidence=["I"],
evidence_card=[2],
)
self.model_stateless.add_cpds(self.cpd_d, self.cpd_i, self.cpd_g,
self.cpd_l, self.cpd_s)
self.writer_stateless = XMLBIFWriter(self.model_stateless)
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 = (190, 69)"],
"dog-out": ["position = (155, 165)"],
"family-out": ["position = (112, 69)"],
"hear-bark": ["position = (154, 241)"],
"light-on": ["position = (73, 165)"],
}
self.model = 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.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 = XMLBIFWriter(model=self.model)
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 = (190, 69)'],
'dog-out': ['position = (155, 165)'],
'family-out': ['position = (112, 69)'],
'hear-bark': ['position = (154, 241)'],
'light-on': ['position = (73, 165)']}
self.model = 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.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 = XMLBIFWriter(model=self.model)
class TestXMLBIFWriterMethodsString(unittest.TestCase):
def setUp(self):
reader = XMLBIFReader(string=TEST_FILE)
self.expected_model = reader.get_model()
self.writer = XMLBIFWriter(self.expected_model)
self.model_stateless = BayesianModel([('D', 'G'), ('I', 'G'),
('G', 'L'), ('I', 'S')])
self.cpd_d = TabularCPD(variable='D',
variable_card=2,
values=[[0.6, 0.4]])
self.cpd_i = TabularCPD(variable='I',
variable_card=2,
values=[[0.7, 0.3]])
self.cpd_g = TabularCPD(variable='G',
variable_card=3,
values=[[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=['I', 'D'],
evidence_card=[2, 2])
self.cpd_l = TabularCPD(variable='L',
variable_card=2,
values=[[0.1, 0.4, 0.99], [0.9, 0.6, 0.01]],
evidence=['G'],
evidence_card=[3])
self.cpd_s = TabularCPD(variable='S',
variable_card=2,
values=[[0.95, 0.2], [0.05, 0.8]],
evidence=['I'],
evidence_card=[2])
self.model_stateless.add_cpds(self.cpd_d, self.cpd_i, self.cpd_g,
self.cpd_l, self.cpd_s)
self.writer_stateless = XMLBIFWriter(self.model_stateless)
def test_write_xmlbif_statefull(self):
self.writer.write_xmlbif("dog_problem_output.xbif")
with open("dog_problem_output.xbif", "r") as f:
file_text = f.read()
reader = XMLBIFReader(string=file_text)
model = reader.get_model()
self.assert_models_equivelent(self.expected_model, model)
os.remove("dog_problem_output.xbif")
def test_write_xmlbif_stateless(self):
self.writer_stateless.write_xmlbif("grade_problem_output.xbif")
with open("grade_problem_output.xbif", 'r') as f:
reader = XMLBIFReader(f)
model = reader.get_model()
self.assert_models_equivelent(self.model_stateless, model)
self.assertDictEqual(
{
'G': ['state0', 'state1', 'state2'],
'I': ['state0', 'state1'],
'D': ['state0', 'state1'],
'S': ['state0', 'state1'],
'L': ['state0', 'state1']
},
model.get_cpds('D').state_names)
os.remove("grade_problem_output.xbif")
def assert_models_equivelent(self, expected, got):
self.assertSetEqual(set(expected.nodes()), set(got.nodes()))
for node in expected.nodes():
self.assertListEqual(expected.get_parents(node),
got.get_parents(node))
cpds_expected = expected.get_cpds(node=node)
cpds_got = got.get_cpds(node=node)
np_test.assert_array_equal(cpds_expected.values, cpds_got.values)
class TestXMLBIFWriterMethodsString(unittest.TestCase):
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 = (190, 69)"],
"dog-out": ["position = (155, 165)"],
"family-out": ["position = (112, 69)"],
"hear-bark": ["position = (154, 241)"],
"light-on": ["position = (73, 165)"],
}
self.model = 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.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 = XMLBIFWriter(model=self.model)
def test_file(self):
self.expected_xml = etree.XML(
"""<BIF version="0.3">
<NETWORK>
<VARIABLE TYPE="nature">
<NAME>bowel-problem</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (190, 69)</PROPERTY>
</VARIABLE>
<VARIABLE TYPE="nature">
<NAME>dog-out</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (155, 165)</PROPERTY>
</VARIABLE>
<VARIABLE TYPE="nature">
<NAME>family-out</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (112, 69)</PROPERTY>
</VARIABLE>
<VARIABLE TYPE="nature">
<NAME>hear-bark</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (154, 241)</PROPERTY>
</VARIABLE>
<VARIABLE TYPE="nature">
<NAME>light-on</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (73, 165)</PROPERTY>
</VARIABLE>
<DEFINITION>
<FOR>bowel-problem</FOR>
<TABLE>0.01 0.99 </TABLE>
</DEFINITION>
<DEFINITION>
<FOR>dog-out</FOR>
<GIVEN>bowel-problem</GIVEN>
<GIVEN>family-out</GIVEN>
<TABLE>0.99 0.01 0.97 0.03 0.9 0.1 0.3 0.7 </TABLE>
</DEFINITION>
<DEFINITION>
<FOR>family-out</FOR>
<TABLE>0.15 0.85 </TABLE>
</DEFINITION>
<DEFINITION>
<FOR>hear-bark</FOR>
<GIVEN>dog-out</GIVEN>
<TABLE>0.7 0.3 0.01 0.99 </TABLE>
</DEFINITION>
<DEFINITION>
<FOR>light-on</FOR>
<GIVEN>family-out</GIVEN>
<TABLE>0.6 0.4 0.05 0.95 </TABLE>
</DEFINITION>
</NETWORK>
</BIF>"""
)
self.maxDiff = None
self.writer.write_xmlbif("test_bif.xml")
with open("test_bif.xml", "r") as myfile:
data = myfile.read()
self.assertEqual(str(self.writer.__str__()[:-1]), str(etree.tostring(self.expected_xml)))
self.assertEqual(str(data), str(etree.tostring(self.expected_xml).decode("utf-8")))
class TestXMLBIFWriterMethodsString(unittest.TestCase):
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 = (190, 69)'],
'dog-out': ['position = (155, 165)'],
'family-out': ['position = (112, 69)'],
'hear-bark': ['position = (154, 241)'],
'light-on': ['position = (73, 165)']
}
self.model = 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.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 = XMLBIFWriter(model=self.model)
def test_file(self):
self.expected_xml = etree.XML("""<BIF version="0.3">
<NETWORK>
<VARIABLE TYPE="nature">
<NAME>bowel-problem</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (190, 69)</PROPERTY>
</VARIABLE>
<VARIABLE TYPE="nature">
<NAME>dog-out</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (155, 165)</PROPERTY>
</VARIABLE>
<VARIABLE TYPE="nature">
<NAME>family-out</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (112, 69)</PROPERTY>
</VARIABLE>
<VARIABLE TYPE="nature">
<NAME>hear-bark</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (154, 241)</PROPERTY>
</VARIABLE>
<VARIABLE TYPE="nature">
<NAME>light-on</NAME>
<OUTCOME>0</OUTCOME>
<OUTCOME>1</OUTCOME>
<PROPERTY>position = (73, 165)</PROPERTY>
</VARIABLE>
<DEFINITION>
<FOR>bowel-problem</FOR>
<TABLE>0.01 0.99 </TABLE>
</DEFINITION>
<DEFINITION>
<FOR>dog-out</FOR>
<GIVEN>bowel-problem</GIVEN>
<GIVEN>family-out</GIVEN>
<TABLE>0.99 0.01 0.97 0.03 0.9 0.1 0.3 0.7 </TABLE>
</DEFINITION>
<DEFINITION>
<FOR>family-out</FOR>
<TABLE>0.15 0.85 </TABLE>
</DEFINITION>
<DEFINITION>
<FOR>hear-bark</FOR>
<GIVEN>dog-out</GIVEN>
<TABLE>0.7 0.3 0.01 0.99 </TABLE>
</DEFINITION>
<DEFINITION>
<FOR>light-on</FOR>
<GIVEN>family-out</GIVEN>
<TABLE>0.6 0.4 0.05 0.95 </TABLE>
</DEFINITION>
</NETWORK>
</BIF>""")
self.maxDiff = None
self.writer.write_xmlbif("test_bif.xml")
with open("test_bif.xml", "r") as myfile:
data = myfile.read()
self.assertEqual(str(self.writer.__str__()[:-1]),
str(etree.tostring(self.expected_xml)))
self.assertEqual(
str(data), str(etree.tostring(self.expected_xml).decode('utf-8')))
class TestXMLBIFWriterMethodsString(unittest.TestCase):
def setUp(self):
reader = XMLBIFReader(string=TEST_FILE)
self.expected_model = reader.get_model()
self.writer = XMLBIFWriter(self.expected_model)
self.model_stateless = BayesianModel([("D", "G"), ("I", "G"),
("G", "L"), ("I", "S")])
self.cpd_d = TabularCPD(variable="D",
variable_card=2,
values=[[0.6, 0.4]])
self.cpd_i = TabularCPD(variable="I",
variable_card=2,
values=[[0.7, 0.3]])
self.cpd_g = TabularCPD(
variable="G",
variable_card=3,
values=[
[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=["I", "D"],
evidence_card=[2, 2],
)
self.cpd_l = TabularCPD(
variable="L",
variable_card=2,
values=[[0.1, 0.4, 0.99], [0.9, 0.6, 0.01]],
evidence=["G"],
evidence_card=[3],
)
self.cpd_s = TabularCPD(
variable="S",
variable_card=2,
values=[[0.95, 0.2], [0.05, 0.8]],
evidence=["I"],
evidence_card=[2],
)
self.model_stateless.add_cpds(self.cpd_d, self.cpd_i, self.cpd_g,
self.cpd_l, self.cpd_s)
self.writer_stateless = XMLBIFWriter(self.model_stateless)
def test_write_xmlbif_statefull(self):
self.writer.write_xmlbif("dog_problem_output.xbif")
with open("dog_problem_output.xbif", "r") as f:
file_text = f.read()
reader = XMLBIFReader(string=file_text)
model = reader.get_model()
self.assert_models_equivelent(self.expected_model, model)
os.remove("dog_problem_output.xbif")
def test_write_xmlbif_stateless(self):
self.writer_stateless.write_xmlbif("grade_problem_output.xbif")
with open("grade_problem_output.xbif", "r") as f:
reader = XMLBIFReader(f)
model = reader.get_model()
self.assert_models_equivelent(self.model_stateless, model)
self.assertDictEqual(
{
"G": ["state0", "state1", "state2"],
"I": ["state0", "state1"],
"D": ["state0", "state1"],
"S": ["state0", "state1"],
"L": ["state0", "state1"],
},
model.get_cpds("D").state_names,
)
os.remove("grade_problem_output.xbif")
def assert_models_equivelent(self, expected, got):
self.assertSetEqual(set(expected.nodes()), set(got.nodes()))
for node in expected.nodes():
self.assertListEqual(list(expected.get_parents(node)),
list(got.get_parents(node)))
cpds_expected = expected.get_cpds(node=node)
cpds_got = got.get_cpds(node=node)
np_test.assert_array_equal(cpds_expected.values, cpds_got.values)
