class TestParis(unittest.TestCase):
def setUp(self):
self.paris = Paris()
self.house_graph = house_graph()
self.karate_club_graph = karate_club_graph()
def test_unknown_types(self):
with self.assertRaises(TypeError):
self.paris.fit(identity(1))
with self.assertRaises(TypeError):
self.paris.fit(identity(2, format='csr'), node_weights=1)
def test_unknown_options(self):
with self.assertRaises(ValueError):
self.paris.fit(identity(2, format='csr'), node_weights='unknown')
def test_house_graph(self):
self.paris.fit(self.house_graph)
self.assertEqual(self.paris.dendrogram_.shape[0], 4)
labels = self.paris.predict(sorted_clusters=True)
self.assertTrue(np.array_equal(labels, np.array([0, 0, 1, 1, 0])))
def test_karate_club_graph(self):
self.paris.fit(self.karate_club_graph)
self.assertEqual(self.paris.dendrogram_.shape[0], 33)
labels = self.paris.predict()
self.assertEqual(np.max(labels), 1)
def fit_paris(self):
""" Uses a super useful library scikit-network to fit a PARIS clusterer on the kNN graph.
PARIS clustering is hierarchical, so it returns a dendrogram instead of clusters. Later we cut the dendrogram.
see: Hierarchical Graph Clustering using Node Pair Sampling by Bonald et al https://arxiv.org/abs/1806.01664"""
if self.verbose:
print('fitting PARIS hierarchical clustering')
paris = Paris()
paris.fit(self.adj)
self.dendrogram = paris.dendrogram_
class ParisClusterer(object):
def __init__(self, featureMatrix):
self.featureMatrix = featureMatrix
def buildAdjacency(self, type='pynndescent', nn=50, metric='dice'):
print('Building nearest neighbor graph (slowest step)...')
if type == 'pynndescent':
nn_index = NNDescent(self.featureMatrix,
n_neighbors=nn,
metric=metric)
n, d = nn_index.neighbor_graph
self.n = n
self.d = d
print('Done')
print('Building weighted, directed adjacency matrix...')
wdAdj = sparse.dok_matrix(
(self.featureMatrix.shape[0], self.featureMatrix.shape[0]),
dtype=float)
for neighbours, distances in tqdm(zip(n, d)):
instanceIndex = neighbours[0]
for neighbourIndex, distance in zip(neighbours[1:], distances[1:]):
wdAdj[instanceIndex,
neighbourIndex] += 1 - distance #similarity = 1-distance
self.wdAdj = sparse.csr_matrix(wdAdj).astype(float)
def fit(self):
self.paris = Paris(engine='numba')
self.paris.fit(self.wdAdj)
def balanced_cut(self, max_cluster_size):
n_nodes = self.paris.dendrogram_.shape[0] + 1
labels = np.zeros(n_nodes, dtype=int)
cluster = {node: [node] for node in range(n_nodes)}
completed_clusters = list()
for t in range(n_nodes - 1):
currentID = n_nodes + t
left = cluster[int(self.paris.dendrogram_[t][0])]
right = cluster[int(self.paris.dendrogram_[t][1])]
if len(left) + len(right) > max_cluster_size:
for clust in [left, right]:
if len(clust) < max_cluster_size:
completed_clusters.append(clust)
cluster[currentID] = cluster.pop(int(
self.paris.dendrogram_[t][0])) + cluster.pop(
int(self.paris.dendrogram_[t][1]))
for count, indices in enumerate(completed_clusters):
labels[indices] = count
self.labels_ = labels
class TestMetrics(unittest.TestCase):
def setUp(self):
self.paris = Paris()
self.karate_club_graph = karate_club_graph()
def test_karate_club_graph(self):
adjacency = self.karate_club_graph
dendrogram = self.paris.fit(adjacency).dendrogram_
tsd = tree_sampling_divergence(adjacency, dendrogram, normalized=True)
self.assertAlmostEqual(tsd, .65, 2)
dc = dasgupta_cost(adjacency, dendrogram, normalized=True)
self.assertAlmostEqual(dc, .33, 2)
class TestParis(unittest.TestCase):
def setUp(self):
self.paris = Paris(engine='python')
self.biparis = BiParis(engine='python')
if is_numba_available:
self.paris_numba = Paris(engine='numba')
self.biparis_numba = BiParis(engine='numba')
else:
with self.assertRaises(ValueError):
Paris(engine='numba')
# noinspection PyTypeChecker
def test_unknown_types(self):
with self.assertRaises(TypeError):
self.paris.fit(sparse.identity(1))
# noinspection DuplicatedCode
def test_undirected(self):
house_graph = house()
if is_numba_available:
self.paris_numba.fit(house_graph)
self.assertEqual(self.paris_numba.dendrogram_.shape[0], 4)
labels = straight_cut(self.paris_numba.dendrogram_,
sorted_clusters=True)
self.assertTrue(np.array_equal(labels, np.array([0, 0, 1, 1, 0])))
self.paris.fit(house_graph)
self.assertEqual(self.paris.dendrogram_.shape[0], 4)
labels = straight_cut(self.paris.dendrogram_, sorted_clusters=True)
self.assertTrue(np.array_equal(labels, np.array([0, 0, 1, 1, 0])))
karate_club_graph = karate_club()
self.paris.fit(karate_club_graph)
self.assertEqual(self.paris.dendrogram_.shape[0], 33)
labels = straight_cut(self.paris.dendrogram_)
self.assertEqual(np.max(labels), 1)
def test_bipartite(self):
star_wars_graph = star_wars_villains()
self.biparis.fit(star_wars_graph)
dendrogram = self.biparis.dendrogram_
self.assertEqual(dendrogram.shape, (6, 4))
if is_numba_available:
self.biparis_numba.fit(star_wars_graph)
dendrogram = self.biparis_numba.dendrogram_
self.assertEqual(dendrogram.shape, (6, 4))
