def test_dot(self):
"""Check that dot works with position hints."""
fragment = DiscreteBayesNet()
fragment.add(Either, [Tuberculosis, LungCancer], "F T T T")
MyAsia = gtsam.symbol('a', 0), 2 # use a symbol!
fragment.add(Tuberculosis, [MyAsia], "99/1 95/5")
fragment.add(LungCancer, [Smoking], "99/1 90/10")
# Make sure we can *update* position hints
writer = gtsam.DotWriter()
ph: dict = writer.positionHints
ph.update({'a': 2}) # hint at symbol position
writer.positionHints = ph
# Check the output of dot
actual = fragment.dot(writer=writer)
expected_result = """\
digraph {
size="5,5";
var3[label="3"];
var4[label="4"];
var5[label="5"];
var6[label="6"];
var6989586621679009792[label="a0", pos="0,2!"];
var4->var6
var6989586621679009792->var3
var3->var5
var6->var5
}"""
self.assertEqual(actual, textwrap.dedent(expected_result))
def test_fragment(self):
"""Test sampling and optimizing for Asia fragment."""
# Create a reverse-topologically sorted fragment:
fragment = DiscreteBayesNet()
fragment.add(Either, [Tuberculosis, LungCancer], "F T T T")
fragment.add(Tuberculosis, [Asia], "99/1 95/5")
fragment.add(LungCancer, [Smoking], "99/1 90/10")
# Create assignment with missing values:
given = DiscreteValues()
for key in [Asia, Smoking]:
given[key[0]] = 0
# Now sample from fragment:
actual = fragment.sample(given)
self.assertEqual(len(actual), 5)
def test_constructor(self):
"""Test constructing a Bayes net."""
bayesNet = DiscreteBayesNet()
Parent, Child = (0, 2), (1, 2)
empty = DiscreteKeys()
prior = DiscreteConditional(Parent, empty, "6/4")
bayesNet.add(prior)
parents = DiscreteKeys()
parents.push_back(Parent)
conditional = DiscreteConditional(Child, parents, "7/3 8/2")
bayesNet.add(conditional)
# Check conversion to factor graph:
fg = DiscreteFactorGraph(bayesNet)
self.assertEqual(fg.size(), 2)
self.assertEqual(fg.at(1).size(), 2)
def test_elimination(self):
"""Test Multifrontal elimination."""
# Define DiscreteKey pairs.
keys = [(j, 2) for j in range(15)]
# Create thin-tree Bayesnet.
bayesNet = DiscreteBayesNet()
bayesNet.add(keys[0], [keys[8], keys[12]], "2/3 1/4 3/2 4/1")
bayesNet.add(keys[1], [keys[8], keys[12]], "4/1 2/3 3/2 1/4")
bayesNet.add(keys[2], [keys[9], keys[12]], "1/4 8/2 2/3 4/1")
bayesNet.add(keys[3], [keys[9], keys[12]], "1/4 2/3 3/2 4/1")
bayesNet.add(keys[4], [keys[10], keys[13]], "2/3 1/4 3/2 4/1")
bayesNet.add(keys[5], [keys[10], keys[13]], "4/1 2/3 3/2 1/4")
bayesNet.add(keys[6], [keys[11], keys[13]], "1/4 3/2 2/3 4/1")
bayesNet.add(keys[7], [keys[11], keys[13]], "1/4 2/3 3/2 4/1")
bayesNet.add(keys[8], [keys[12], keys[14]], "T 1/4 3/2 4/1")
bayesNet.add(keys[9], [keys[12], keys[14]], "4/1 2/3 F 1/4")
bayesNet.add(keys[10], [keys[13], keys[14]], "1/4 3/2 2/3 4/1")
bayesNet.add(keys[11], [keys[13], keys[14]], "1/4 2/3 3/2 4/1")
bayesNet.add(keys[12], [keys[14]], "3/1 3/1")
bayesNet.add(keys[13], [keys[14]], "1/3 3/1")
bayesNet.add(keys[14], "1/3")
# Create a factor graph out of the Bayes net.
factorGraph = DiscreteFactorGraph(bayesNet)
# Create a BayesTree out of the factor graph.
ordering = Ordering()
for j in range(15):
ordering.push_back(j)
bayesTree = factorGraph.eliminateMultifrontal(ordering)
# Uncomment these for visualization:
# print(bayesTree)
# for key in range(15):
# bayesTree[key].printSignature()
# bayesTree.saveGraph("test_DiscreteBayesTree.dot")
self.assertFalse(bayesTree.empty())
self.assertEqual(12, bayesTree.size())
# The root is P( 8 12 14), we can retrieve it by key:
root = bayesTree[8]
self.assertIsInstance(root, DiscreteBayesTreeClique)
self.assertTrue(root.isRoot())
self.assertIsInstance(root.conditional(), DiscreteConditional)
def test_Asia(self):
"""Test full Asia example."""
asia = DiscreteBayesNet()
asia.add(Asia, "99/1")
asia.add(Smoking, "50/50")
asia.add(Tuberculosis, [Asia], "99/1 95/5")
asia.add(LungCancer, [Smoking], "99/1 90/10")
asia.add(Bronchitis, [Smoking], "70/30 40/60")
asia.add(Either, [Tuberculosis, LungCancer], "F T T T")
asia.add(XRay, [Either], "95/5 2/98")
asia.add(Dyspnea, [Either, Bronchitis], "9/1 2/8 3/7 1/9")
# Convert to factor graph
fg = DiscreteFactorGraph(asia)
# Create solver and eliminate
ordering = Ordering()
for j in range(8):
ordering.push_back(j)
chordal = fg.eliminateSequential(ordering)
expected2 = DiscreteDistribution(Bronchitis, "11/9")
self.gtsamAssertEquals(chordal.at(7), expected2)
# solve
actualMPE = fg.optimize()
expectedMPE = DiscreteValues()
for key in [
Asia, Dyspnea, XRay, Tuberculosis, Smoking, Either, LungCancer,
Bronchitis
]:
expectedMPE[key[0]] = 0
self.assertEqual(list(actualMPE.items()), list(expectedMPE.items()))
# Check value for MPE is the same
self.assertAlmostEqual(asia(actualMPE), fg(actualMPE))
# add evidence, we were in Asia and we have dyspnea
fg.add(Asia, "0 1")
fg.add(Dyspnea, "0 1")
# solve again, now with evidence
actualMPE2 = fg.optimize()
expectedMPE2 = DiscreteValues()
for key in [XRay, Tuberculosis, Either, LungCancer]:
expectedMPE2[key[0]] = 0
for key in [Asia, Dyspnea, Smoking, Bronchitis]:
expectedMPE2[key[0]] = 1
self.assertEqual(list(actualMPE2.items()), list(expectedMPE2.items()))
# now sample from it
chordal2 = fg.eliminateSequential(ordering)
actualSample = chordal2.sample()
self.assertEqual(len(actualSample), 8)