def test_reduce_cpd_statename(self):
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],
state_names=self.sn2)
cpd.reduce([('diff', 'high')])
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variables, ['grade', 'intel'])
np_test.assert_array_equal(
cpd.get_values(),
np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]]))
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],
state_names=self.sn2)
cpd.reduce([('diff', 0)])
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variables, ['grade', 'intel'])
np_test.assert_array_equal(
cpd.get_values(),
np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]]))
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],
state_names=self.sn2)
cpd = cpd.reduce([('diff', 'high')], inplace=False)
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variables, ['grade', 'intel'])
np_test.assert_array_equal(
cpd.get_values(),
np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]]))
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],
state_names=self.sn2)
cpd = cpd.reduce([('diff', 0)], inplace=False)
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variables, ['grade', 'intel'])
np_test.assert_array_equal(
cpd.get_values(),
np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]]))
def test_reduce_cpd_statename(self):
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],
state_names=self.sn2)
cpd.reduce([('diff', 'high')])
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variables, ['grade', 'intel'])
np_test.assert_array_equal(cpd.get_values(), np.array([[0.1, 0.1, 0.1],
[0.1, 0.1, 0.1],
[0.8, 0.8, 0.8]]))
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],
state_names=self.sn2)
cpd.reduce([('diff', 0)])
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variables, ['grade', 'intel'])
np_test.assert_array_equal(cpd.get_values(), np.array([[0.1, 0.1, 0.1],
[0.1, 0.1, 0.1],
[0.8, 0.8, 0.8]]))
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],
state_names=self.sn2)
cpd = cpd.reduce([('diff', 'high')], inplace=False)
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variables, ['grade', 'intel'])
np_test.assert_array_equal(cpd.get_values(), np.array([[0.1, 0.1, 0.1],
[0.1, 0.1, 0.1],
[0.8, 0.8, 0.8]]))
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],
state_names=self.sn2)
cpd = cpd.reduce([('diff', 0)], inplace=False)
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variables, ['grade', 'intel'])
np_test.assert_array_equal(cpd.get_values(), np.array([[0.1, 0.1, 0.1],
[0.1, 0.1, 0.1],
[0.8, 0.8, 0.8]]))
def tabularDf(cpd: TabularCPD) -> DataFrame:
'''
Converts a pgmpy TabularCPD to pandas DataFrame for nicer viewing
'''
# Get names of variables (evidence vars) whose combos of states will go on top of the data frame
evidenceVars: List[Name] = list(cpd.state_names.keys())[1:]
# Get all state names mapped to each variable
states: List[State] = list(cpd.state_names.values())
# Create combinations of states to go on the horizontal part of the CPD dataframe
evidenceStateCombos: List[Tuple[State, State]] = list(
itertools.product(*states[1:]))
# note: Avoiding error thrown when passing empty list of tuples to MultiIndex
topColNames = [''] if evidenceVars == [] else pd.MultiIndex.from_tuples(
evidenceStateCombos, names=evidenceVars)
df: DataFrame = DataFrame(data=cpd.get_values(),
index=states[0],
columns=topColNames)
df.index.name = cpd.variable # the query var name
# NOTE: if no evidence vars then do not transpose, else we do transpose
if evidenceVars == []:
return df
return df.transpose(
) # else if there are evidence vars then we CAN transpose.
def test_reduce_cpd_statename(self):
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],
state_names=self.sn2,
)
cpd.reduce([("diff", "high")])
self.assertEqual(cpd.variable, "grade")
self.assertEqual(cpd.variables, ["grade", "intel"])
np_test.assert_array_equal(
cpd.get_values(),
np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]]),
)
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],
)
cpd.reduce([("diff", 0)])
self.assertEqual(cpd.variable, "grade")
self.assertEqual(cpd.variables, ["grade", "intel"])
np_test.assert_array_equal(
cpd.get_values(),
np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]]),
)
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],
state_names=self.sn2,
)
cpd = cpd.reduce([("diff", "high")], inplace=False)
self.assertEqual(cpd.variable, "grade")
self.assertEqual(cpd.variables, ["grade", "intel"])
np_test.assert_array_equal(
cpd.get_values(),
np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]]),
)
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],
)
cpd = cpd.reduce([("diff", 0)], inplace=False)
self.assertEqual(cpd.variable, "grade")
self.assertEqual(cpd.variables, ["grade", "intel"])
np_test.assert_array_equal(
cpd.get_values(),
np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]]),
)
