def setUp(self):
nodes = {'c': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(c) Brain Tumor',
'YPOS': '11935',
'XPOS': '15250',
'TYPE': 'discrete'},
'a': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(a) Metastatic Cancer',
'YPOS': '10465',
'XPOS': '13495',
'TYPE': 'discrete'},
'b': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(b) Serum Calcium Increase',
'YPOS': '11965',
'XPOS': '11290',
'TYPE': 'discrete'},
'e': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(e) Papilledema',
'YPOS': '13240',
'XPOS': '17305',
'TYPE': 'discrete'},
'd': {'STATES': ['Present', 'Absent'],
'DESCRIPTION': '(d) Coma',
'YPOS': '12985',
'XPOS': '13960',
'TYPE': 'discrete'}}
model = BayesianModel([('b', 'd'), ('a', 'b'), ('a', 'c'), ('c', 'd'), ('c', 'e')])
cpd_distribution = {'a': {'TYPE': 'discrete', 'DPIS': np.array([[0.2, 0.8]])},
'e': {'TYPE': 'discrete', 'DPIS': np.array([[0.8, 0.2],
[0.6, 0.4]]), 'CONDSET': ['c'], 'CARDINALITY': [2]},
'b': {'TYPE': 'discrete', 'DPIS': np.array([[0.8, 0.2],
[0.2, 0.8]]), 'CONDSET': ['a'], 'CARDINALITY': [2]},
'c': {'TYPE': 'discrete', 'DPIS': np.array([[0.2, 0.8],
[0.05, 0.95]]), 'CONDSET': ['a'], 'CARDINALITY': [2]},
'd': {'TYPE': 'discrete', 'DPIS': np.array([[0.8, 0.2],
[0.9, 0.1],
[0.7, 0.3],
[0.05, 0.95]]), 'CONDSET': ['b', 'c'], 'CARDINALITY': [2, 2]}}
tabular_cpds = []
for var, values in cpd_distribution.items():
evidence = values['CONDSET'] if 'CONDSET' in values else []
cpd = values['DPIS']
evidence_card = values['CARDINALITY'] if 'CARDINALITY' in values else []
states = nodes[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 nodes.items():
model.node[var] = properties
self.maxDiff = None
self.writer = XMLBeliefNetwork.XBNWriter(model=model)
def setUp(self):
string = """<ANALYSISNOTEBOOK NAME="Notebook.Cancer Example From Neapolitan" ROOT="Cancer">
<BNMODEL NAME="Cancer">
<STATICPROPERTIES>
<FORMAT VALUE="MSR DTAS XML"/>
<VERSION VALUE="0.2"/>
<CREATOR VALUE="Microsoft Research DTAS"/>
</STATICPROPERTIES>
<VARIABLES>
<VAR NAME="a" TYPE="discrete" XPOS="13495" YPOS="10465">
<DESCRIPTION>(a) Metastatic Cancer</DESCRIPTION>
<STATENAME>Present</STATENAME>
<STATENAME>Absent</STATENAME>
</VAR>
<VAR NAME="b" TYPE="discrete" XPOS="11290" YPOS="11965">
<DESCRIPTION>(b) Serum Calcium Increase</DESCRIPTION>
<STATENAME>Present</STATENAME>
<STATENAME>Absent</STATENAME>
</VAR>
<VAR NAME="c" TYPE="discrete" XPOS="15250" YPOS="11935">
<DESCRIPTION>(c) Brain Tumor</DESCRIPTION>
<STATENAME>Present</STATENAME>
<STATENAME>Absent</STATENAME>
</VAR>
<VAR NAME="d" TYPE="discrete" XPOS="13960" YPOS="12985">
<DESCRIPTION>(d) Coma</DESCRIPTION>
<STATENAME>Present</STATENAME>
<STATENAME>Absent</STATENAME>
</VAR>
<VAR NAME="e" TYPE="discrete" XPOS="17305" YPOS="13240">
<DESCRIPTION>(e) Papilledema</DESCRIPTION>
<STATENAME>Present</STATENAME>
<STATENAME>Absent</STATENAME>
</VAR>
</VARIABLES>
<STRUCTURE>
<ARC PARENT="a" CHILD="b"/>
<ARC PARENT="a" CHILD="c"/>
<ARC PARENT="b" CHILD="d"/>
<ARC PARENT="c" CHILD="d"/>
<ARC PARENT="c" CHILD="e"/>
</STRUCTURE>
<DISTRIBUTIONS>
<DIST TYPE="discrete">
<PRIVATE NAME="a"/>
<DPIS>
<DPI> 0.2 0.8</DPI>
</DPIS>
</DIST>
<DIST TYPE="discrete">
<CONDSET>
<CONDELEM NAME="a"/>
</CONDSET>
<PRIVATE NAME="b"/>
<DPIS>
<DPI INDEXES=" 0 "> 0.8 0.2</DPI>
<DPI INDEXES=" 1 "> 0.2 0.8</DPI>
</DPIS>
</DIST>
<DIST TYPE="discrete">
<CONDSET>
<CONDELEM NAME="a"/>
</CONDSET>
<PRIVATE NAME="c"/>
<DPIS>
<DPI INDEXES=" 0 "> 0.2 0.8</DPI>
<DPI INDEXES=" 1 "> 0.05 0.95</DPI>
</DPIS>
</DIST>
<DIST TYPE="discrete">
<CONDSET>
<CONDELEM NAME="b"/>
<CONDELEM NAME="c"/>
</CONDSET>
<PRIVATE NAME="d"/>
<DPIS>
<DPI INDEXES=" 0 0 "> 0.8 0.2</DPI>
<DPI INDEXES=" 0 1 "> 0.9 0.1</DPI>
<DPI INDEXES=" 1 0 "> 0.7 0.3</DPI>
<DPI INDEXES=" 1 1 "> 0.05 0.95</DPI>
</DPIS>
</DIST>
<DIST TYPE="discrete">
<CONDSET>
<CONDELEM NAME="c"/>
</CONDSET>
<PRIVATE NAME="e"/>
<DPIS>
<DPI INDEXES=" 0 "> 0.8 0.2</DPI>
<DPI INDEXES=" 1 "> 0.6 0.4</DPI>
</DPIS>
</DIST>
</DISTRIBUTIONS>
</BNMODEL>
</ANALYSISNOTEBOOK>"""
self.reader_string = XMLBeliefNetwork.XBNReader(string=string)
self.reader_file = XMLBeliefNetwork.XBNReader(path=StringIO(string))
def setUp(self):
nodes = {
"c": {
"STATES": ["Present", "Absent"],
"DESCRIPTION": "(c) Brain Tumor",
"YPOS": "11935",
"XPOS": "15250",
"TYPE": "discrete",
},
"a": {
"STATES": ["Present", "Absent"],
"DESCRIPTION": "(a) Metastatic Cancer",
"YPOS": "10465",
"XPOS": "13495",
"TYPE": "discrete",
},
"b": {
"STATES": ["Present", "Absent"],
"DESCRIPTION": "(b) Serum Calcium Increase",
"YPOS": "11965",
"XPOS": "11290",
"TYPE": "discrete",
},
"e": {
"STATES": ["Present", "Absent"],
"DESCRIPTION": "(e) Papilledema",
"YPOS": "13240",
"XPOS": "17305",
"TYPE": "discrete",
},
"f": {
"STATES": ["Present", "Absent"],
"DESCRIPTION": "(f) Asthma",
"YPOS": "10489",
"XPOS": "13440",
"TYPE": "discrete",
},
"d": {
"STATES": ["Present", "Absent"],
"DESCRIPTION": "(d) Coma",
"YPOS": "12985",
"XPOS": "13960",
"TYPE": "discrete",
},
}
model = BayesianModel()
model.add_nodes_from(["a", "b", "c", "d", "e", "f"])
model.add_edges_from([("b", "d"), ("a", "b"), ("a", "c"), ("c", "d"),
("c", "e")])
cpd_distribution = {
"a": {
"TYPE": "discrete",
"DPIS": np.array([[0.2, 0.8]])
},
"e": {
"TYPE": "discrete",
"DPIS": np.array([[0.8, 0.2], [0.6, 0.4]]),
"CONDSET": ["c"],
"CARDINALITY": [2],
},
"f": {
"TYPE": "discrete",
"DPIS": np.array([[0.3, 0.7]])
},
"b": {
"TYPE": "discrete",
"DPIS": np.array([[0.8, 0.2], [0.2, 0.8]]),
"CONDSET": ["a"],
"CARDINALITY": [2],
},
"c": {
"TYPE": "discrete",
"DPIS": np.array([[0.2, 0.8], [0.05, 0.95]]),
"CONDSET": ["a"],
"CARDINALITY": [2],
},
"d": {
"TYPE":
"discrete",
"DPIS":
np.array([[0.8, 0.2], [0.9, 0.1], [0.7, 0.3], [0.05, 0.95]]),
"CONDSET": ["b", "c"],
"CARDINALITY": [2, 2],
},
}
tabular_cpds = []
for var, values in cpd_distribution.items():
evidence = values["CONDSET"] if "CONDSET" in values else []
cpd = values["DPIS"]
evidence_card = values[
"CARDINALITY"] if "CARDINALITY" in values else []
states = nodes[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 nodes.items():
model.nodes[var] = properties
else:
for var, properties in nodes.items():
model._node[var] = properties
self.maxDiff = None
self.writer = XMLBeliefNetwork.XBNWriter(model=model)