class TestUndirectedGraphTriangulation(unittest.TestCase):
def setUp(self):
self.graph = MarkovModel()
def test_check_clique(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'a')])
self.assertTrue(self.graph.check_clique(['a', 'b', 'c']))
def test_is_triangulated(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'a')])
self.assertTrue(self.graph.is_triangulated())
def test_triangulation_h1_inplace(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)
self.graph.triangulate(heuristic='H1', inplace=True)
self.assertTrue(self.graph.is_triangulated())
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()),
[['a', 'b'], ['a', 'c'], ['a', 'd'], ['b', 'c'], ['c', 'd']])
def test_triangulation_h2_inplace(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)
self.graph.triangulate(heuristic='H2', inplace=True)
self.assertTrue(self.graph.is_triangulated())
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()),
[['a', 'b'], ['a', 'c'], ['a', 'd'], ['b', 'c'], ['c', 'd']])
def test_triangulation_h3_inplace(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)
self.graph.triangulate(heuristic='H3', inplace=True)
self.assertTrue(self.graph.is_triangulated())
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_triangulation_h4_inplace(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)
self.graph.triangulate(heuristic='H4', inplace=True)
self.assertTrue(self.graph.is_triangulated())
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_triangulation_h5_inplace(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)
self.graph.triangulate(heuristic='H4', inplace=True)
self.assertTrue(self.graph.is_triangulated())
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_triangulation_h6_inplace(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)
self.graph.triangulate(heuristic='H4', inplace=True)
self.assertTrue(self.graph.is_triangulated())
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_cardinality_mismatch_raises_error(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
factor_list = [
DiscreteFactor(edge, [2, 2], np.random.rand(4))
for edge in self.graph.edges()
]
self.graph.add_factors(*factor_list)
self.graph.add_factors(
DiscreteFactor(['a', 'b'], [2, 3], np.random.rand(6)))
self.assertRaises(ValueError, self.graph.triangulate)
def test_triangulation_h1_create_new(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)
H = self.graph.triangulate(heuristic='H1', inplace=True)
self.assertListEqual(
hf.recursive_sorted(H.edges()),
[['a', 'b'], ['a', 'c'], ['a', 'd'], ['b', 'c'], ['c', 'd']])
def test_triangulation_h2_create_new(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)
H = self.graph.triangulate(heuristic='H2', inplace=True)
self.assertListEqual(
hf.recursive_sorted(H.edges()),
[['a', 'b'], ['a', 'c'], ['a', 'd'], ['b', 'c'], ['c', 'd']])
def test_triangulation_h3_create_new(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)
H = self.graph.triangulate(heuristic='H3', inplace=True)
self.assertListEqual(
hf.recursive_sorted(H.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_triangulation_h4_create_new(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)
H = self.graph.triangulate(heuristic='H4', inplace=True)
self.assertListEqual(
hf.recursive_sorted(H.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_triangulation_h5_create_new(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)
H = self.graph.triangulate(heuristic='H5', inplace=True)
self.assertListEqual(
hf.recursive_sorted(H.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_triangulation_h6_create_new(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)
H = self.graph.triangulate(heuristic='H6', inplace=True)
self.assertListEqual(
hf.recursive_sorted(H.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_copy(self):
# Setup the original graph
self.graph.add_nodes_from(['a', 'b'])
self.graph.add_edges_from([('a', 'b')])
# Generate the copy
copy = self.graph.copy()
# Ensure the copied model is correct
self.assertTrue(copy.check_model())
# Basic sanity checks to ensure the graph was copied correctly
self.assertEqual(len(copy.nodes()), 2)
self.assertListEqual(copy.neighbors('a'), ['b'])
self.assertListEqual(copy.neighbors('b'), ['a'])
# Modify the original graph ...
self.graph.add_nodes_from(['c'])
self.graph.add_edges_from([('c', 'b')])
# ... and ensure none of those changes get propagated
self.assertEqual(len(copy.nodes()), 2)
self.assertListEqual(copy.neighbors('a'), ['b'])
self.assertListEqual(copy.neighbors('b'), ['a'])
with self.assertRaises(nx.NetworkXError):
copy.neighbors('c')
# Ensure the copy has no factors at this point
self.assertEqual(len(copy.get_factors()), 0)
# Add factors to the original graph
phi1 = DiscreteFactor(['a', 'b'], [2, 2], [[0.3, 0.7], [0.9, 0.1]])
self.graph.add_factors(phi1)
# The factors should not get copied over
with self.assertRaises(AssertionError):
self.assertListEqual(copy.get_factors(), self.graph.get_factors())
# Create a fresh copy
del copy
copy = self.graph.copy()
self.assertListEqual(copy.get_factors(), self.graph.get_factors())
# If we change factors in the original, it should not be passed to the clone
phi1.values = np.array([[0.5, 0.5], [0.5, 0.5]])
self.assertNotEqual(self.graph.get_factors(), copy.get_factors())
# Start with a fresh copy
del copy
self.graph.add_nodes_from(['d'])
copy = self.graph.copy()
# Ensure an unconnected node gets copied over as well
self.assertEqual(len(copy.nodes()), 4)
self.assertListEqual(self.graph.neighbors('a'), ['b'])
self.assertTrue('a' in self.graph.neighbors('b'))
self.assertTrue('c' in self.graph.neighbors('b'))
self.assertListEqual(self.graph.neighbors('c'), ['b'])
self.assertListEqual(self.graph.neighbors('d'), [])
# Verify that changing the copied model should not update the original
copy.add_nodes_from(['e'])
self.assertListEqual(copy.neighbors('e'), [])
with self.assertRaises(nx.NetworkXError):
self.graph.neighbors('e')
# Verify that changing edges in the copy doesn't create edges in the original
copy.add_edges_from([('d', 'b')])
self.assertTrue('a' in copy.neighbors('b'))
self.assertTrue('c' in copy.neighbors('b'))
self.assertTrue('d' in copy.neighbors('b'))
self.assertTrue('a' in self.graph.neighbors('b'))
self.assertTrue('c' in self.graph.neighbors('b'))
self.assertFalse('d' in self.graph.neighbors('b'))
# If we remove factors from the copied model, it should not reflect in the original
copy.remove_factors(phi1)
self.assertEqual(len(self.graph.get_factors()), 1)
self.assertEqual(len(copy.get_factors()), 0)
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 = 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_check_cardinality(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.get_cardinality,
check_cardinality=True)
phi2 = DiscreteFactor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi2)
self.assertRaises(ValueError,
self.graph.get_cardinality,
check_cardinality=True)
phi3 = DiscreteFactor(['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 = 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_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(['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_model2(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(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 TestMarkovModelCreation(unittest.TestCase):
def setUp(self):
self.graph = MarkovModel()
def test_class_init_without_data(self):
self.assertIsInstance(self.graph, MarkovModel)
def test_class_init_with_data_string(self):
self.g = MarkovModel([('a', 'b'), ('b', 'c')])
self.assertListEqual(sorted(self.g.nodes()), ['a', 'b', 'c'])
self.assertListEqual(hf.recursive_sorted(self.g.edges()),
[['a', 'b'], ['b', 'c']])
def test_class_init_with_data_nonstring(self):
self.g = MarkovModel([(1, 2), (2, 3)])
def test_add_node_string(self):
self.graph.add_node('a')
self.assertListEqual(self.graph.nodes(), ['a'])
def test_add_node_nonstring(self):
self.graph.add_node(1)
def test_add_nodes_from_string(self):
self.graph.add_nodes_from(['a', 'b', 'c', 'd'])
self.assertListEqual(sorted(self.graph.nodes()), ['a', 'b', 'c', 'd'])
def test_add_nodes_from_non_string(self):
self.graph.add_nodes_from([1, 2, 3, 4])
def test_add_edge_string(self):
self.graph.add_edge('d', 'e')
self.assertListEqual(sorted(self.graph.nodes()), ['d', 'e'])
self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
[['d', 'e']])
self.graph.add_nodes_from(['a', 'b', 'c'])
self.graph.add_edge('a', 'b')
self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
[['a', 'b'], ['d', 'e']])
def test_add_edge_nonstring(self):
self.graph.add_edge(1, 2)
def test_add_edge_selfloop(self):
self.assertRaises(ValueError, self.graph.add_edge, 'a', 'a')
def test_add_edges_from_string(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
self.assertListEqual(sorted(self.graph.nodes()), ['a', 'b', 'c'])
self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
[['a', 'b'], ['b', 'c']])
self.graph.add_nodes_from(['d', 'e', 'f'])
self.graph.add_edges_from([('d', 'e'), ('e', 'f')])
self.assertListEqual(sorted(self.graph.nodes()),
['a', 'b', 'c', 'd', 'e', 'f'])
self.assertListEqual(
hf.recursive_sorted(self.graph.edges()),
hf.recursive_sorted([('a', 'b'), ('b', 'c'), ('d', 'e'),
('e', 'f')]))
def test_add_edges_from_nonstring(self):
self.graph.add_edges_from([(1, 2), (2, 3)])
def test_add_edges_from_self_loop(self):
self.assertRaises(ValueError, self.graph.add_edges_from, [('a', 'a')])
def test_number_of_neighbors(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
self.assertEqual(len(self.graph.neighbors('b')), 2)
def tearDown(self):
del self.graph