class TestEmbeddings(unittest.TestCase):
def setUp(self):
row, col, w = zip(*((1, 2, 1.0), (1, 3, 3.0), (2, 3, 1.0), (2, 6, 0.5), (3, 4, 1.0), (4, 5, 1.0), (4, 7, -1.0),
(5, 6, 0.0), (6, 5, 0.1), (6, 2, 0.1)))
dir_edges = DirectedEdges(sp.csr_matrix((w, (row, col)), shape=(8, 8)))
self.toy_directed = Network(np.arange(8), dir_edges)
row, col, w = zip(*((1, 2, 1.0), (1, 3, 3.0), (2, 3, 1.0), (2, 6, 0.5), (3, 4, 1.0), (4, 5, 1.0), (4, 7, -1.0),
(5, 6, 0.1)))
undir_edges = UndirectedEdges(sp.csr_matrix((w, (row, col)), shape=(8, 8)))
self.toy_undirected = Network(np.arange(8), undir_edges)
def test_node_probas(self):
""" Test that node probabilities get calculated correctly """
n2v = Node2Vec()
# nowhere to go from isolated node
self.assertEqual(len(n2v.node_probas(self.toy_directed, 0)), 0)
# should not have division by zero when weights of edges to neighbors sum to 0
self.assertDictEqual(n2v.node_probas(self.toy_directed, 5), {6: 1.0})
probas = n2v.node_probas(self.toy_directed, 4)
self.assertAlmostEqual(probas[5], 0.881, places=3)
self.assertAlmostEqual(probas[7], 0.119, places=3)
self.assertDictEqual(n2v.node_probas(self.toy_directed, 1), {2: 0.25, 3: 0.75})
self.assertDictEqual(n2v.node_probas(self.toy_undirected, 3), {1: 0.6, 2: 0.2, 4: 0.2})
def test_edge_probas(self):
""" Test that edge probabilities get calculated appropriately based on shortest distance between previous node
and next node (equations in 'Search bias' section of node2vec paper) """
n2v = Node2Vec(p=0.8, q=0.5)
edge_probas = n2v.edge_probas(self.toy_directed, 3, 4)
self.assertAlmostEqual(edge_probas[(4, 5)], 0.982, places=3) # d_tx = 2
self.assertAlmostEqual(edge_probas[(4, 7)], 0.018, places=3) # d_tx = 2
edge_probas = n2v.edge_probas(self.toy_directed, 1, 2)
self.assertAlmostEqual(edge_probas[(2, 3)], 0.5) # d_tx = 1
edge_probas = n2v.edge_probas(self.toy_directed, 5, 6)
self.assertAlmostEqual(edge_probas[(6, 5)], 0.385, places=3) # d_tx = 0
edge_probas = n2v.edge_probas(self.toy_undirected, 1, 2)
self.assertAlmostEqual(edge_probas[(2, 1)], 0.385, places=3) # d_tx = 0
self.assertAlmostEqual(edge_probas[(2, 3)], 0.308, places=3) # d_tx = 1
self.assertAlmostEqual(edge_probas[(2, 6)], 0.308, places=3) # d_tx = 2
def test_call(self):
n2v = Node2Vec(num_walks=10, walk_len=80, emb_size=300)
embeddings = n2v(self.toy_directed)
self.assertEqual(embeddings.X.shape, (self.toy_directed.number_of_nodes(), 300))
# check that domain is extended and that the existing attributes do not change places
empty = np.array([[] for _ in range(8)])
data = Table(Domain([ContinuousVariable("var1")]), np.array([[i] for i in range(8)]), empty, empty)
toy_net_with_data = Network(data, self.toy_directed.edges[0])
extended_data = n2v(toy_net_with_data)
self.assertEqual(extended_data.X.shape, (toy_net_with_data.number_of_nodes(), 1 + 300))
np.testing.assert_array_almost_equal(extended_data.X[:, 0], np.arange(8))
def test_call(self):
n2v = Node2Vec(num_walks=10, walk_len=80, emb_size=300)
embeddings = n2v(self.toy_directed)
self.assertEqual(embeddings.X.shape, (self.toy_directed.number_of_nodes(), 300))
# check that domain is extended and that the existing attributes do not change places
empty = np.array([[] for _ in range(8)])
data = Table(Domain([ContinuousVariable("var1")]), np.array([[i] for i in range(8)]), empty, empty)
toy_net_with_data = Network(data, self.toy_directed.edges[0])
extended_data = n2v(toy_net_with_data)
self.assertEqual(extended_data.X.shape, (toy_net_with_data.number_of_nodes(), 1 + 300))
np.testing.assert_array_almost_equal(extended_data.X[:, 0], np.arange(8))
def summarize_(net: Network):
n = net.number_of_nodes()
if len(net.edges) == 1:
nettype = ['Network', 'Directed network'][net.edges[0].directed]
details = f"<nobr>{nettype} with {n} nodes " \
f"and {net.number_of_edges()} edges</nobr>"
else:
details = f"<nobr>Network with {n} nodes"
if net.edges:
details += " and {len(net.edges)} edge types:</nobr><ul>" + "".join(
f"<li>{len(edges)} edges, "
f"{['undirected', 'directed'][edges.directed]}</li>"
for edges in net.edges)
return PartialSummary(n, details)
