class TestDiffusion(unittest.TestCase):
def test_approximate(self):
self.closeness = Closeness(method='approximate', n_jobs=-1)
scores = self.closeness.fit_transform(house())
self.assertEqual((np.round(scores, 2) == [0.67, 0.8, 0.67, 0.67,
0.8]).sum(), 5)
def test_connected(self):
self.closeness = Closeness()
adjacency = sparse.identity(2, format='csr')
with self.assertRaises(ValueError):
self.closeness.fit(adjacency)
def test_parallel(self):
adjacency = test_graph()
n = adjacency.shape[0]
closeness = Closeness(method='approximate')
scores1 = closeness.fit_transform(adjacency)
closeness = Closeness(method='approximate', n_jobs=-1)
scores2 = closeness.fit_transform(adjacency)
self.assertEqual(scores1.shape, (n,))
self.assertAlmostEqual(np.linalg.norm(scores1 - scores2), 0)
def test_disconnected(self):
adjacency = test_graph_disconnect()
closeness = Closeness()
with self.assertRaises(ValueError):
closeness.fit(adjacency)
def test_params(self):
with self.assertRaises(ValueError):
adjacency = test_graph()
Closeness(method='toto').fit(adjacency)
def test_connected(self):
self.closeness = Closeness()
adjacency = sparse.identity(2, format='csr')
with self.assertRaises(ValueError):
self.closeness.fit(adjacency)
def test_approximate(self):
self.closeness = Closeness(method='approximate', n_jobs=-1)
scores = self.closeness.fit_transform(house())
self.assertEqual((np.round(scores, 2) == [0.67, 0.8, 0.67, 0.67,
0.8]).sum(), 5)