class TestMarkovModelMethods(unittest.TestCase):
def setUp(self):
self.graph = MarkovModel()
def test_get_cardinality(self):
self.graph.add_edges_from([("a", "b"), ("b", "c"), ("c", "d"),
("d", "a")])
self.assertDictEqual(self.graph.get_cardinality(), {})
phi1 = DiscreteFactor(["a", "b"], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertDictEqual(self.graph.get_cardinality(), {"a": 1, "b": 2})
self.graph.remove_factors(phi1)
self.assertDictEqual(self.graph.get_cardinality(), {})
phi1 = DiscreteFactor(["a", "b"], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(["c", "d"], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertDictEqual(self.graph.get_cardinality(), {
"d": 2,
"a": 2,
"b": 2,
"c": 1
})
phi3 = DiscreteFactor(["d", "a"], [1, 2], np.random.rand(2))
self.graph.add_factors(phi3)
self.assertDictEqual(self.graph.get_cardinality(), {
"d": 1,
"c": 1,
"b": 2,
"a": 2
})
self.graph.remove_factors(phi1, phi2, phi3)
self.assertDictEqual(self.graph.get_cardinality(), {})
def test_get_cardinality_with_node(self):
self.graph.add_edges_from([("a", "b"), ("b", "c"), ("c", "d"),
("d", "a")])
phi1 = DiscreteFactor(["a", "b"], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(["c", "d"], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertEqual(self.graph.get_cardinality("a"), 2)
self.assertEqual(self.graph.get_cardinality("b"), 2)
self.assertEqual(self.graph.get_cardinality("c"), 1)
self.assertEqual(self.graph.get_cardinality("d"), 2)
def test_check_model(self):
self.graph.add_edges_from([("a", "b"), ("b", "c"), ("c", "d"),
("d", "a")])
phi1 = DiscreteFactor(["a", "b"], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertRaises(ValueError, self.graph.check_model)
phi2 = DiscreteFactor(["a", "c"], [1, 2], np.random.rand(2))
self.graph.add_factors(phi2)
self.assertRaises(ValueError, self.graph.check_model)
def test_check_model1(self):
self.graph.add_edges_from([("a", "b"), ("b", "c"), ("c", "d"),
("d", "a")])
phi1 = DiscreteFactor(["a", "b"], [1, 2], np.random.rand(2))
phi2 = DiscreteFactor(["c", "b"], [3, 2], np.random.rand(6))
phi3 = DiscreteFactor(["c", "d"], [3, 4], np.random.rand(12))
phi4 = DiscreteFactor(["d", "a"], [4, 1], np.random.rand(4))
self.graph.add_factors(phi1, phi2, phi3, phi4)
self.assertTrue(self.graph.check_model())
self.graph.remove_factors(phi1, phi4)
phi1 = DiscreteFactor(["a", "b"], [4, 2], np.random.rand(8))
self.graph.add_factors(phi1)
self.assertTrue(self.graph.check_model())
def test_check_model2(self):
self.graph.add_edges_from([("a", "b"), ("b", "c"), ("c", "d"),
("d", "a")])
phi1 = DiscreteFactor(["a", "b"], [1, 2], np.random.rand(2))
phi2 = DiscreteFactor(["b", "c"], [3, 3], np.random.rand(9))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
phi3 = DiscreteFactor(["c", "a"], [4, 4], np.random.rand(16))
self.graph.add_factors(phi3)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi3)
phi2 = DiscreteFactor(["b", "c"], [2, 3], np.random.rand(6))
phi3 = DiscreteFactor(["c", "d"], [3, 4], np.random.rand(12))
phi4 = DiscreteFactor(["d", "a"], [4, 3], np.random.rand(12))
self.graph.add_factors(phi2, phi3, phi4)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2, phi3, phi4)
phi2 = DiscreteFactor(["a", "b"], [1, 3], np.random.rand(3))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
def test_check_model3(self):
self.graph.add_edges_from([("a", "b"), ("b", "c"), ("c", "d"),
("d", "a")])
phi1 = DiscreteFactor(["a", "c"], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1)
phi1 = DiscreteFactor(["a", "b"], [1, 2], np.random.rand(2))
phi2 = DiscreteFactor(["a", "c"], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1, phi2)
phi1 = DiscreteFactor(["a", "b"], [1, 2], np.random.rand(2))
phi2 = DiscreteFactor(["b", "c"], [2, 3], np.random.rand(6))
phi3 = DiscreteFactor(["c", "d"], [3, 4], np.random.rand(12))
phi4 = DiscreteFactor(["d", "a"], [4, 1], np.random.rand(4))
phi5 = DiscreteFactor(["d", "b"], [4, 2], np.random.rand(8))
self.graph.add_factors(phi1, phi2, phi3, phi4, phi5)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1, phi2, phi3, phi4, phi5)
def test_factor_graph(self):
phi1 = DiscreteFactor(["Alice", "Bob"], [3, 2], np.random.rand(6))
phi2 = DiscreteFactor(["Bob", "Charles"], [2, 2], np.random.rand(4))
self.graph.add_edges_from([("Alice", "Bob"), ("Bob", "Charles")])
self.graph.add_factors(phi1, phi2)
factor_graph = self.graph.to_factor_graph()
self.assertIsInstance(factor_graph, FactorGraph)
self.assertListEqual(
sorted(factor_graph.nodes()),
["Alice", "Bob", "Charles", "phi_Alice_Bob", "phi_Bob_Charles"],
)
self.assertListEqual(
hf.recursive_sorted(factor_graph.edges()),
[
["Alice", "phi_Alice_Bob"],
["Bob", "phi_Alice_Bob"],
["Bob", "phi_Bob_Charles"],
["Charles", "phi_Bob_Charles"],
],
)
self.assertListEqual(factor_graph.get_factors(), [phi1, phi2])
def test_factor_graph_raises_error(self):
self.graph.add_edges_from([("Alice", "Bob"), ("Bob", "Charles")])
self.assertRaises(ValueError, self.graph.to_factor_graph)
def test_junction_tree(self):
self.graph.add_edges_from([("a", "b"), ("b", "c"), ("c", "d"),
("d", "a")])
phi1 = DiscreteFactor(["a", "b"], [2, 3], np.random.rand(6))
phi2 = DiscreteFactor(["b", "c"], [3, 4], np.random.rand(12))
phi3 = DiscreteFactor(["c", "d"], [4, 5], np.random.rand(20))
phi4 = DiscreteFactor(["d", "a"], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(
hf.recursive_sorted(junction_tree.nodes()),
[["a", "b", "d"], ["b", "c", "d"]],
)
self.assertEqual(len(junction_tree.edges()), 1)
def test_junction_tree_single_clique(self):
self.graph.add_edges_from([("x1", "x2"), ("x2", "x3"), ("x1", "x3")])
phi = [
DiscreteFactor(edge, [2, 2], np.random.rand(4))
for edge in self.graph.edges()
]
self.graph.add_factors(*phi)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(hf.recursive_sorted(junction_tree.nodes()),
[["x1", "x2", "x3"]])
factors = junction_tree.get_factors()
self.assertEqual(factors[0], factor_product(*phi))
def test_markov_blanket(self):
self.graph.add_edges_from([("a", "b"), ("b", "c")])
self.assertListEqual(list(self.graph.markov_blanket("a")), ["b"])
self.assertListEqual(sorted(self.graph.markov_blanket("b")),
["a", "c"])
def test_local_independencies(self):
self.graph.add_edges_from([("a", "b"), ("b", "c")])
independencies = self.graph.get_local_independencies()
self.assertIsInstance(independencies, Independencies)
self.assertEqual(independencies, Independencies(["a", "c", "b"]))
def test_bayesian_model(self):
self.graph.add_edges_from([("a", "b"), ("b", "c"), ("c", "d"),
("d", "a")])
phi1 = DiscreteFactor(["a", "b"], [2, 3], np.random.rand(6))
phi2 = DiscreteFactor(["b", "c"], [3, 4], np.random.rand(12))
phi3 = DiscreteFactor(["c", "d"], [4, 5], np.random.rand(20))
phi4 = DiscreteFactor(["d", "a"], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
bm = self.graph.to_bayesian_model()
self.assertIsInstance(bm, BayesianModel)
self.assertListEqual(sorted(bm.nodes()), ["a", "b", "c", "d"])
self.assertTrue(nx.is_chordal(bm.to_undirected()))
def tearDown(self):
del self.graph
class TestMarkovModelMethods(unittest.TestCase):
def setUp(self):
self.graph = MarkovModel()
def test_get_cardinality(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
self.assertDictEqual(self.graph.get_cardinality(), {})
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertDictEqual(self.graph.get_cardinality(), {'a': 1, 'b': 2})
self.graph.remove_factors(phi1)
self.assertDictEqual(self.graph.get_cardinality(), {})
phi1 = Factor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = Factor(['c', 'd'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertDictEqual(self.graph.get_cardinality(), {'d': 2, 'a': 2, 'b': 2, 'c': 1})
phi3 = Factor(['d', 'a'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi3)
self.assertDictEqual(self.graph.get_cardinality(), {'d': 1, 'c': 1, 'b': 2, 'a': 2})
self.graph.remove_factors(phi1, phi2, phi3)
self.assertDictEqual(self.graph.get_cardinality(), {})
def test_get_cardinality_check_cardinality(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertRaises(ValueError, self.graph.get_cardinality, check_cardinality=True)
phi2 = Factor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi2)
self.assertRaises(ValueError, self.graph.get_cardinality, check_cardinality=True)
phi3 = Factor(['c', 'd'], [2, 2], np.random.rand(4))
self.graph.add_factors(phi3)
self.assertDictEqual(self.graph.get_cardinality(check_cardinality=True), {'d': 2, 'c': 2, 'b': 2, 'a': 1})
def test_check_model(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = Factor(['c', 'b'], [3, 2], np.random.rand(6))
phi3 = Factor(['c', 'd'], [3, 4], np.random.rand(12))
phi4 = Factor(['d', 'a'], [4, 1], np.random.rand(4))
self.graph.add_factors(phi1, phi2, phi3, phi4)
self.assertTrue(self.graph.check_model())
self.graph.remove_factors(phi1, phi4)
phi1 = Factor(['a', 'b'], [4, 2], np.random.rand(8))
self.graph.add_factors(phi1)
self.assertTrue(self.graph.check_model())
def test_check_model1(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = Factor(['b', 'c'], [3, 3], np.random.rand(9))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
phi3 = Factor(['c', 'a'], [4, 4], np.random.rand(16))
self.graph.add_factors(phi3)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi3)
phi2 = Factor(['b', 'c'], [2, 3], np.random.rand(6))
phi3 = Factor(['c', 'd'], [3, 4], np.random.rand(12))
phi4 = Factor(['d', 'a'], [4, 3], np.random.rand(12))
self.graph.add_factors(phi2, phi3, phi4)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2, phi3, phi4)
phi2 = Factor(['a', 'b'], [1, 3], np.random.rand(3))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
def test_check_model2(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1)
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = Factor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1, phi2)
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = Factor(['b', 'c'], [2, 3], np.random.rand(6))
phi3 = Factor(['c', 'd'], [3, 4], np.random.rand(12))
phi4 = Factor(['d', 'a'], [4, 1], np.random.rand(4))
phi5 = Factor(['d', 'b'], [4, 2], np.random.rand(8))
self.graph.add_factors(phi1, phi2, phi3, phi4, phi5)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1, phi2, phi3, phi4, phi5)
def test_factor_graph(self):
from pgmpy.models import FactorGraph
phi1 = Factor(['Alice', 'Bob'], [3, 2], np.random.rand(6))
phi2 = Factor(['Bob', 'Charles'], [2, 2], np.random.rand(4))
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles')])
self.graph.add_factors(phi1, phi2)
factor_graph = self.graph.to_factor_graph()
self.assertIsInstance(factor_graph, FactorGraph)
self.assertListEqual(sorted(factor_graph.nodes()),
['Alice', 'Bob', 'Charles', 'phi_Alice_Bob',
'phi_Bob_Charles'])
self.assertListEqual(hf.recursive_sorted(factor_graph.edges()),
[['Alice', 'phi_Alice_Bob'], ['Bob', 'phi_Alice_Bob'],
['Bob', 'phi_Bob_Charles'], ['Charles', 'phi_Bob_Charles']])
self.assertListEqual(factor_graph.get_factors(), [phi1, phi2])
def test_factor_graph_raises_error(self):
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles')])
self.assertRaises(ValueError, self.graph.to_factor_graph)
def test_junction_tree(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(hf.recursive_sorted(junction_tree.nodes()),
[['a', 'b', 'd'], ['b', 'c', 'd']])
self.assertEqual(len(junction_tree.edges()), 1)
def test_junction_tree_single_clique(self):
from pgmpy.factors import factor_product
self.graph.add_edges_from([('x1','x2'), ('x2', 'x3'), ('x1', 'x3')])
phi = [Factor(edge, [2, 2], np.random.rand(4)) for edge in self.graph.edges()]
self.graph.add_factors(*phi)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(hf.recursive_sorted(junction_tree.nodes()),
[['x1', 'x2', 'x3']])
factors = junction_tree.get_factors()
self.assertEqual(factors[0], factor_product(*phi))
def test_markov_blanket(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
self.assertListEqual(self.graph.markov_blanket('a'), ['b'])
self.assertListEqual(sorted(self.graph.markov_blanket('b')),
['a', 'c'])
def test_local_independencies(self):
from pgmpy.independencies import Independencies
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
independencies = self.graph.get_local_independencies()
self.assertIsInstance(independencies, Independencies)
self.assertEqual(len(independencies.get_assertions()), 2)
string = ''
for assertion in sorted(independencies.get_assertions(),
key=lambda x: list(x.event1)):
string += str(assertion) + '\n'
self.assertEqual(string, '(a _|_ c | b)\n(c _|_ a | b)\n')
def test_bayesian_model(self):
from pgmpy.models import BayesianModel
import networkx as nx
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
bm = self.graph.to_bayesian_model()
self.assertIsInstance(bm, BayesianModel)
self.assertListEqual(sorted(bm.nodes()), ['a', 'b', 'c', 'd'])
self.assertTrue(nx.is_chordal(bm.to_undirected()))
def tearDown(self):
del self.graph
class TestMarkovModelMethods(unittest.TestCase):
def setUp(self):
self.graph = MarkovModel()
def test_get_cardinality(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
self.assertDictEqual(self.graph.get_cardinality(), {})
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertDictEqual(self.graph.get_cardinality(), {'a': 1, 'b': 2})
self.graph.remove_factors(phi1)
self.assertDictEqual(self.graph.get_cardinality(), {})
phi1 = Factor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = Factor(['c', 'd'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertDictEqual(self.graph.get_cardinality(), {
'd': 2,
'a': 2,
'b': 2,
'c': 1
})
phi3 = Factor(['d', 'a'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi3)
self.assertDictEqual(self.graph.get_cardinality(), {
'd': 1,
'c': 1,
'b': 2,
'a': 2
})
self.graph.remove_factors(phi1, phi2, phi3)
self.assertDictEqual(self.graph.get_cardinality(), {})
def test_get_cardinality_check_cardinality(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertRaises(ValueError,
self.graph.get_cardinality,
check_cardinality=True)
phi2 = Factor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi2)
self.assertRaises(ValueError,
self.graph.get_cardinality,
check_cardinality=True)
phi3 = Factor(['c', 'd'], [2, 2], np.random.rand(4))
self.graph.add_factors(phi3)
self.assertDictEqual(
self.graph.get_cardinality(check_cardinality=True), {
'd': 2,
'c': 2,
'b': 2,
'a': 1
})
def test_check_model(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = Factor(['c', 'b'], [3, 2], np.random.rand(6))
phi3 = Factor(['c', 'd'], [3, 4], np.random.rand(12))
phi4 = Factor(['d', 'a'], [4, 1], np.random.rand(4))
self.graph.add_factors(phi1, phi2, phi3, phi4)
self.assertTrue(self.graph.check_model())
self.graph.remove_factors(phi1, phi4)
phi1 = Factor(['a', 'b'], [4, 2], np.random.rand(8))
self.graph.add_factors(phi1)
self.assertTrue(self.graph.check_model())
def test_check_model1(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = Factor(['b', 'c'], [3, 3], np.random.rand(9))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
phi3 = Factor(['c', 'a'], [4, 4], np.random.rand(16))
self.graph.add_factors(phi3)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi3)
phi2 = Factor(['b', 'c'], [2, 3], np.random.rand(6))
phi3 = Factor(['c', 'd'], [3, 4], np.random.rand(12))
phi4 = Factor(['d', 'a'], [4, 3], np.random.rand(12))
self.graph.add_factors(phi2, phi3, phi4)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2, phi3, phi4)
phi2 = Factor(['a', 'b'], [1, 3], np.random.rand(3))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
def test_check_model2(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1)
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = Factor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1, phi2)
phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = Factor(['b', 'c'], [2, 3], np.random.rand(6))
phi3 = Factor(['c', 'd'], [3, 4], np.random.rand(12))
phi4 = Factor(['d', 'a'], [4, 1], np.random.rand(4))
phi5 = Factor(['d', 'b'], [4, 2], np.random.rand(8))
self.graph.add_factors(phi1, phi2, phi3, phi4, phi5)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi1, phi2, phi3, phi4, phi5)
def test_factor_graph(self):
phi1 = Factor(['Alice', 'Bob'], [3, 2], np.random.rand(6))
phi2 = Factor(['Bob', 'Charles'], [2, 2], np.random.rand(4))
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles')])
self.graph.add_factors(phi1, phi2)
factor_graph = self.graph.to_factor_graph()
self.assertIsInstance(factor_graph, FactorGraph)
self.assertListEqual(
sorted(factor_graph.nodes()),
['Alice', 'Bob', 'Charles', 'phi_Alice_Bob', 'phi_Bob_Charles'])
self.assertListEqual(
hf.recursive_sorted(factor_graph.edges()),
[['Alice', 'phi_Alice_Bob'], ['Bob', 'phi_Alice_Bob'],
['Bob', 'phi_Bob_Charles'], ['Charles', 'phi_Bob_Charles']])
self.assertListEqual(factor_graph.get_factors(), [phi1, phi2])
def test_factor_graph_raises_error(self):
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles')])
self.assertRaises(ValueError, self.graph.to_factor_graph)
def test_junction_tree(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(hf.recursive_sorted(junction_tree.nodes()),
[['a', 'b', 'd'], ['b', 'c', 'd']])
self.assertEqual(len(junction_tree.edges()), 1)
def test_junction_tree_single_clique(self):
self.graph.add_edges_from([('x1', 'x2'), ('x2', 'x3'), ('x1', 'x3')])
phi = [
Factor(edge, [2, 2], np.random.rand(4))
for edge in self.graph.edges()
]
self.graph.add_factors(*phi)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(hf.recursive_sorted(junction_tree.nodes()),
[['x1', 'x2', 'x3']])
factors = junction_tree.get_factors()
self.assertEqual(factors[0], factor_product(*phi))
def test_markov_blanket(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
self.assertListEqual(self.graph.markov_blanket('a'), ['b'])
self.assertListEqual(sorted(self.graph.markov_blanket('b')),
['a', 'c'])
def test_local_independencies(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
independencies = self.graph.get_local_independencies()
self.assertIsInstance(independencies, Independencies)
self.assertEqual(independencies, Independencies(['a', 'c', 'b']))
def test_bayesian_model(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
bm = self.graph.to_bayesian_model()
self.assertIsInstance(bm, BayesianModel)
self.assertListEqual(sorted(bm.nodes()), ['a', 'b', 'c', 'd'])
self.assertTrue(nx.is_chordal(bm.to_undirected()))
def tearDown(self):
del self.graph
class TestMarkovModelMethods(unittest.TestCase):
def setUp(self):
self.graph = MarkovModel()
def test_factor_graph(self):
from pgmpy.models import FactorGraph
phi1 = Factor(['Alice', 'Bob'], [3, 2], np.random.rand(6))
phi2 = Factor(['Bob', 'Charles'], [3, 2], np.random.rand(6))
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles')])
self.graph.add_factors(phi1, phi2)
factor_graph = self.graph.to_factor_graph()
self.assertIsInstance(factor_graph, FactorGraph)
self.assertListEqual(
sorted(factor_graph.nodes()),
['Alice', 'Bob', 'Charles', 'phi_Alice_Bob', 'phi_Bob_Charles'])
self.assertListEqual(
hf.recursive_sorted(factor_graph.edges()),
[['Alice', 'phi_Alice_Bob'], ['Bob', 'phi_Alice_Bob'],
['Bob', 'phi_Bob_Charles'], ['Charles', 'phi_Bob_Charles']])
self.assertListEqual(factor_graph.get_factors(), [phi1, phi2])
def test_factor_graph_raises_error(self):
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles')])
self.assertRaises(ValueError, self.graph.to_factor_graph)
def test_junction_tree(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(hf.recursive_sorted(junction_tree.nodes()),
[['a', 'b', 'd'], ['b', 'c', 'd']])
self.assertEqual(len(junction_tree.edges()), 1)
def test_junction_tree_single_clique(self):
from pgmpy.factors import factor_product
self.graph.add_edges_from([('x1', 'x2'), ('x2', 'x3'), ('x1', 'x3')])
phi = [
Factor(edge, [2, 2], np.random.rand(4))
for edge in self.graph.edges()
]
self.graph.add_factors(*phi)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(hf.recursive_sorted(junction_tree.nodes()),
[['x1', 'x2', 'x3']])
factors = junction_tree.get_factors()
self.assertEqual(factors[0], factor_product(*phi))
def test_markov_blanket(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
self.assertListEqual(self.graph.markov_blanket('a'), ['b'])
self.assertListEqual(sorted(self.graph.markov_blanket('b')),
['a', 'c'])
def test_local_independencies(self):
from pgmpy.independencies import Independencies
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
independencies = self.graph.get_local_independencies()
self.assertIsInstance(independencies, Independencies)
self.assertEqual(len(independencies.get_assertions()), 2)
string = ''
for assertion in sorted(independencies.get_assertions(),
key=lambda x: list(x.event1)):
string += str(assertion) + '\n'
self.assertEqual(string, '(a _|_ c | b)\n(c _|_ a | b)\n')
def test_bayesian_model(self):
from pgmpy.models import BayesianModel
import networkx as nx
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
bm = self.graph.to_bayesian_model()
self.assertIsInstance(bm, BayesianModel)
self.assertListEqual(sorted(bm.nodes()), ['a', 'b', 'c', 'd'])
self.assertTrue(nx.is_chordal(bm.to_undirected()))
def tearDown(self):
del self.graph
