def test_node_indices():
# e1
# graph: n1 -> n2
# e2 | /
# v / (e4 (n2->n3))
# n3 -> n4
# e3
n1 = Node(1.0)
n2 = Node(2.0)
n3 = Node(3.0)
n4 = Node(4.0)
e1 = Edge([n1, n2])
e2 = Edge([n1, n3])
e3 = Edge([n3, n4])
e4 = Edge([n2, n3])
g1 = Graph([n1, n2, n3, n4], [e1, e2, e3, e4])
assert g1.next_node_index == 4
n1, n2, n3, n4 = g1.get_node_list()
assert n1.get_attribute("node_index") == 0
assert n2.get_attribute("node_index") == 1
assert n3.get_attribute("node_index") == 2
assert n4.get_attribute("node_index") == 3
def test_degree_undirected_graph():
# e1
# graph: n1 -- n2
# e2 | /
# | /
# n3 -- n4
# e3
n1 = Node(1.0)
n2 = Node(2.0)
n3 = Node(3.0)
n4 = Node(4.0)
e1 = Edge([n1, n2], weight=4.0, directed=False)
e2 = Edge([n1, n3], weight=5.0, directed=False)
e3 = Edge([n3, n4], weight=6.0, directed=False)
e4 = Edge([n2, n3], weight=7.0, directed=False)
g1 = Graph([n1, n2, n3, n4], [e1, e2, e3, e4], directed=False)
D1 = g1.get_D()
adjacency_matrix = np.array([
[0.0, 4.0, 5.0, 0.0],
[4.0, 0.0, 7.0, 0.0],
[5.0, 7.0, 0.0, 6.0],
[0.0, 0.0, 6.0, 0.0],
])
np.testing.assert_allclose(D1, np.diag(adjacency_matrix.sum(1)))
np.testing.assert_allclose(D1, np.diag(adjacency_matrix.sum(0)))
def test_adjacency_directed_graph():
# e1
# graph: n1 -> n2
# e2 | /
# v / (e4 (n2->n3))
# n3 -> n4
# e3
n1 = Node(1.0)
n2 = Node(2.0)
n3 = Node(3.0)
n4 = Node(4.0)
e1 = Edge([n1, n2], weight=4.0)
e2 = Edge([n1, n3], weight=5.0)
e3 = Edge([n3, n4], weight=6.0)
e4 = Edge([n2, n3], weight=7.0)
g1 = Graph([n1, n2, n3, n4], [e1, e2, e3, e4])
W1 = g1.get_W()
adjacency_matrix = np.array([
[0.0, 4.0, 5.0, 0.0],
[0.0, 0.0, 7.0, 0.0],
[0.0, 0.0, 0.0, 6.0],
[0.0, 0.0, 0.0, 0.0],
])
np.testing.assert_allclose(W1, adjacency_matrix)
def test_simple_undirected_graph_connections():
# e1
# graph: n1 -- n2
# e2 | /
# | /
# n3 -- n4
# e3
n1 = Node(1.0)
n2 = Node(2.0)
n3 = Node(3.0)
n4 = Node(4.0)
e1 = Edge([n1, n2], directed=False)
e2 = Edge([n1, n3], directed=False)
e3 = Edge([n3, n4], directed=False)
e4 = Edge([n2, n3], directed=False)
assert n1.get_attribute("in_degree") is None
assert n1.get_attribute("out_degree") is None
assert n1.get_attribute("degree") == 2
assert n2.get_attribute("in_degree") is None
assert n2.get_attribute("out_degree") is None
assert n2.get_attribute("degree") == 2
assert n3.get_attribute("in_degree") is None
assert n3.get_attribute("out_degree") is None
assert n3.get_attribute("degree") == 3
assert n4.get_attribute("in_degree") is None
assert n4.get_attribute("out_degree") is None
assert n4.get_attribute("degree") == 1
g1 = Graph([n1, n2, n3, n4], [e1, e2, e3, e4], directed=False)
n1, n2, n3, n4 = g1.get_node_list()
assert n1.get_attribute("in_degree") is None
assert n1.get_attribute("out_degree") is None
assert n1.get_attribute("degree") == 2
assert n2.get_attribute("in_degree") is None
assert n2.get_attribute("out_degree") is None
assert n2.get_attribute("degree") == 2
assert n3.get_attribute("in_degree") is None
assert n3.get_attribute("out_degree") is None
assert n3.get_attribute("degree") == 3
assert n4.get_attribute("in_degree") is None
assert n4.get_attribute("out_degree") is None
assert n4.get_attribute("degree") == 1
def test_simple_directed_graph_connections():
# e1
# graph: n1 -> n2
# e2 | /
# v / (e4 (n2->n3))
# n3 -> n4
# e3
n1 = Node(1.0)
n2 = Node(2.0)
n3 = Node(3.0)
n4 = Node(4.0)
e1 = Edge([n1, n2], directed=True)
e2 = Edge([n1, n3], directed=True)
e3 = Edge([n3, n4], directed=True)
e4 = Edge([n2, n3], directed=True)
assert n1.get_attribute("in_degree") == 0
assert n1.get_attribute("out_degree") == 2
assert n1.get_attribute("degree") == 2
assert n2.get_attribute("in_degree") == 1
assert n2.get_attribute("out_degree") == 1
assert n2.get_attribute("degree") == 2
assert n3.get_attribute("in_degree") == 2
assert n3.get_attribute("out_degree") == 1
assert n3.get_attribute("degree") == 3
assert n4.get_attribute("in_degree") == 1
assert n4.get_attribute("out_degree") == 0
assert n4.get_attribute("degree") == 1
g1 = Graph([n1, n2, n3, n4], [e1, e2, e3, e4])
n1, n2, n3, n4 = g1.get_node_list()
assert n1.get_attribute("in_degree") == 0
assert n1.get_attribute("out_degree") == 2
assert n1.get_attribute("degree") == 2
assert n2.get_attribute("in_degree") == 1
assert n2.get_attribute("out_degree") == 1
assert n2.get_attribute("degree") == 2
assert n3.get_attribute("in_degree") == 2
assert n3.get_attribute("out_degree") == 1
assert n3.get_attribute("degree") == 3
assert n4.get_attribute("in_degree") == 1
assert n4.get_attribute("out_degree") == 0
assert n4.get_attribute("degree") == 1
def test_undirected_graph_raise_error_for_directed_edge():
n1 = Node(1.0)
n2 = Node(2.0)
e1 = Edge([n1, n2], directed=True)
with pytest.raises(RuntimeError):
Graph(nodes=[n1, n2], edges=[e1], directed=False)
def nextTask(self):
id = -1
while True:
if len(self.localJobQueue) > 0 and random() < 0.7:
id = self.localJobQueue.pop()
elif len(self.IDQueue) == 0:
self.IDQueue.append(
id=self.taskClient.getRedis(self.taskID).spop(100))
if len(self.IDQueue) != 0:
id = self.IDQueue.get()
if not (id in self.recentProcessedIDs):
break
else:
id = -1
if id == -1:
return [id, Graph()]
return [id, self.graphCache.egoNetwork(id)]
def egoNetwork(self, nodeID):
nodeID = strToUnicode(nodeID)
rtnGraph = Graph()
edgeID = 0
if self.existNode(nodeID):
neighbours = self.loadNeighbours(nodeID)
for neighbour in neighbours:
neighbour = strToUnicode(neighbour)
rtnGraph.addEdge(edgeID, nodeID, neighbour, 1.0)
edgeID += 1
cNeighbours = self.loadNeighbours(neighbour)
for cNeighbour in cNeighbours:
if cNeighbour in neighbours:
rtnGraph.addEdge(edgeID, neighbour, cNeighbour, 1.0)
edgeID += 1
return rtnGraph
def test_simple_undirected_graph_neighbors():
# e1
# graph: n1 -- n2
# e2 | /
# | /
# n3 -- n4
# e3
n1 = Node(1.0)
n2 = Node(2.0)
n3 = Node(3.0)
n4 = Node(4.0)
e1 = Edge([n1, n2], directed=False)
e2 = Edge([n1, n3], directed=False)
e3 = Edge([n3, n4], directed=False)
e4 = Edge([n2, n3], directed=False)
g1 = Graph([n1, n2, n3, n4], [e1, e2, e3, e4], directed=False)
neighbors = g1.get_neighbors(n1)
assert n1 not in neighbors
assert n2 in neighbors
assert n3 in neighbors
assert n4 not in neighbors
neighbors = g1.get_neighbors(n2)
assert n1 in neighbors
assert n2 not in neighbors
assert n3 in neighbors
assert n4 not in neighbors
neighbors = g1.get_neighbors(n3)
assert n1 in neighbors
assert n2 in neighbors
assert n3 not in neighbors
assert n4 in neighbors
neighbors = g1.get_neighbors(n4)
assert n1 not in neighbors
assert n2 not in neighbors
assert n3 in neighbors
assert n4 not in neighbors
def test_simple_directed_graph_neighbors():
# e1
# graph: n1 -> n2
# e2 | /
# v / (e4 (n2->n3))
# n3 -> n4
# e3
n1 = Node(1.0)
n2 = Node(2.0)
n3 = Node(3.0)
n4 = Node(4.0)
e1 = Edge([n1, n2])
e2 = Edge([n1, n3])
e3 = Edge([n3, n4])
e4 = Edge([n2, n3])
g1 = Graph([n1, n2, n3, n4], [e1, e2, e3, e4])
neighbors = g1.get_neighbors(n1)
assert n1 not in neighbors
assert n2 in neighbors
assert n3 in neighbors
assert n4 not in neighbors
neighbors = g1.get_neighbors(n2)
assert n1 not in neighbors
assert n2 not in neighbors
assert n3 in neighbors
assert n4 not in neighbors
neighbors = g1.get_neighbors(n3)
assert n1 not in neighbors
assert n2 not in neighbors
assert n3 not in neighbors
assert n4 in neighbors
neighbors = g1.get_neighbors(n4)
assert n1 not in neighbors
assert n2 not in neighbors
assert n3 not in neighbors
assert n4 not in neighbors
def visualize_graph_2d(
points: np.array, graph: Graph, ax, title: str = '', annotate: bool = False
):
"""
visualize a 2D graph in a matplotlib-axis
:param points: points that are scattered
:param graph: graph that is plotted
:param ax: a matplotlib axis the data is plotted on
:param title: a title for the figure
:param annotate: whether to annotate the nodes with their corresponding index
:return: matplotlib axis
Example
::
from cluster.utils import visualize_graph_2d
from cluster.cluster import SpectralClustering
# cluster the dataset
spectral_clustering = SpectralClustering(k=2, graph_param=5)
spectral_clustering.cluster(points=points)
labels = spectral_clustering.get_labels()
# visualize the graph
fig, ax = plt.subplots()
visualize_graph_2d(points, spectral_clustering.get_graph(), ax, 'kNN graph with 5 neighbors')
plt.show()
.. plot::
import numpy as np
from cluster.utils import visualize_graph_2d
from cluster.cluster import SpectralClustering
np.random.seed(42)
N1 = 10
N2 = 10
r1 = 1 / 4
r2 = 3 / 4
x1 = 2 * np.pi * np.random.rand(N1)
x2 = 2 * np.pi * np.random.rand(N2)
r1 = r1 + np.random.rand(N1) * 0.1
r2 = r2 + np.random.rand(N2) * 0.1
points = np.vstack([np.concatenate([r1 * np.cos(x1), r2 * np.cos(x2)]),
np.concatenate([r1 * np.sin(x1), r2 * np.sin(x2)])]).T
# cluster the dataset
spectral_clustering = SpectralClustering(k=2, graph_param=5)
spectral_clustering.cluster(points=points)
labels = spectral_clustering.get_labels()
# visualize the graph
fig, ax = plt.subplots()
visualize_graph_2d(points, spectral_clustering.get_graph(), ax, 'kNN graph with 5 neighbors')
plt.show()
"""
for i in range(len(graph.get_edge_list())):
edge = graph.get_edge_list()[i]
left = points[edge.get_from_node().get_attribute("node_index")]
right = points[edge.get_to_node().get_attribute("node_index")]
ax.plot([left[0], right[0]], [left[1], right[1]], color="lightblue")
ax.scatter(
points[:, 0],
points[:, 1],
s=5,
color="blue",
zorder=10 * len(graph.get_edge_list()),
)
if annotate:
for node in graph.get_node_list():
idx = node.get_attribute("node_index")
ax.annotate(idx, (points[idx, 0] + 1e-2, points[idx, 1] + 1e-2))
ax.set_title(title)
return ax