def testGenerate(self):
numFeatures = 1
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 0
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), 0)
ell = 5
graph.removeAllEdges()
generator.setEll(ell)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), 0)
#Now test case where we m != 0
ell = 2
m = 1
graph.removeAllEdges()
generator.setEll(ell)
generator.setM(m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), (numVertices-ell)*m)
m = 2
graph.removeAllEdges()
generator.setM(m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), (numVertices-ell)*m)
def testGenerate(self):
numFeatures = 1
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
ell = 2
m = 0
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), 0)
ell = 5
graph.removeAllEdges()
generator.setEll(ell)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), 0)
#Now test case where we m != 0
ell = 2
m = 1
graph.removeAllEdges()
generator.setEll(ell)
generator.setM(m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), (numVertices - ell) * m)
m = 2
graph.removeAllEdges()
generator.setM(m)
graph = generator.generate(graph)
self.assertEquals(graph.getNumEdges(), (numVertices - ell) * m)
def testGenerateGraph(self):
numFeatures = 0
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
alpha1 = 10.0
alpha2 = 20.0
p = 0.001
dim = 2
generator = GeometricRandomGenerator(graph)
graph = generator.generateGraph(alpha1, p, dim)
numEdges1 = graph.getNumEdges()
#Check no self edges
for i in range(numVertices):
self.assertTrue(graph.getEdge(i, i) == None)
graph.removeAllEdges()
graph = generator.generateGraph(alpha2, p, dim)
numEdges2 = graph.getNumEdges()
#self.assertTrue(numEdges1 >= numEdges2)
logging.debug(numEdges1)
logging.debug(numEdges2)
for i in range(numVertices):
self.assertTrue(graph.getEdge(i, i) == None)
#Test case with p=0 and alpha huge
p = 0.0
alpha = 100.0
graph.removeAllEdges()
graph = generator.generateGraph(alpha, p, dim)
self.assertEquals(graph.getNumEdges(), 0)
#When alpha=0, should get max edges
alpha = 0.0
graph.removeAllEdges()
graph = generator.generateGraph(alpha, p, dim)
#self.assertEquals(graph.getNumEdges(), int(0.5*(numVertices + numVertices**2) - numVertices))
#TODO: Test variations in dimension
"""
def testGenerateGraph(self):
numFeatures = 0
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
alpha1 = 10.0
alpha2 = 20.0
p = 0.001
dim = 2
generator = GeometricRandomGenerator(graph)
graph = generator.generateGraph(alpha1, p, dim)
numEdges1 = graph.getNumEdges()
#Check no self edges
for i in range(numVertices):
self.assertTrue(graph.getEdge(i, i) == None)
graph.removeAllEdges()
graph = generator.generateGraph(alpha2, p, dim)
numEdges2 = graph.getNumEdges()
#self.assertTrue(numEdges1 >= numEdges2)
logging.debug(numEdges1)
logging.debug(numEdges2)
for i in range(numVertices):
self.assertTrue(graph.getEdge(i, i) == None)
#Test case with p=0 and alpha huge
p = 0.0
alpha = 100.0
graph.removeAllEdges()
graph = generator.generateGraph(alpha, p, dim)
self.assertEquals(graph.getNumEdges(), 0)
#When alpha=0, should get max edges
alpha = 0.0
graph.removeAllEdges()
graph = generator.generateGraph(alpha, p, dim)
#self.assertEquals(graph.getNumEdges(), int(0.5*(numVertices + numVertices**2) - numVertices))
#TODO: Test variations in dimension
"""
def testGraphDisplay(self):
try:
import networkx
import matplotlib
except ImportError as error:
logging.debug(error)
return
#Show
numFeatures = 1
numVertices = 20
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
logging.debug((graph.getNumEdges()))
nxGraph = graph.toNetworkXGraph()
nodePositions = networkx.spring_layout(nxGraph)
nodesAndEdges = networkx.draw_networkx(nxGraph, pos=nodePositions)
ax = matplotlib.pyplot.axes()
ax.set_xticklabels([])
ax.set_yticklabels([])
def testGenerate2(self):
numVertices = 20
graph = SparseGraph(GeneralVertexList(numVertices))
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
self.assertTrue((graph.getNumEdges() - p*numVertices*numVertices/2) < 8)
def readFromFile(self, fileName):
"""
Read vertices and edges of the graph from the given file name. The file
must have as its first line "Vertices" followed by a list of
vertex indices (one per line). Then the lines following "Arcs" or "Edges"
have a list of pairs of vertex indices represented directed or undirected
edges.
"""
infile = open(fileName, "r")
line = infile.readline()
line = infile.readline()
ind = 0
vertexIdDict = {}
while infile and line != "Edges" and line != "Arcs":
vertexIdDict[int(line)] = ind
line = infile.readline().strip()
ind += 1
if line == "Edges":
undirected = True
elif line == "Arcs":
undirected = False
else:
raise ValueError("Unknown edge types: " + line)
numVertices = len(vertexIdDict)
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
sGraph = SparseGraph(vList, undirected)
line = infile.readline()
while line:
s = line.split()
try:
i = vertexIdDict[int(s[0].strip(',').strip())]
j = vertexIdDict[int(s[1].strip(',').strip())]
k = float(s[2].strip(',').strip())
except KeyError:
print("Vertex not found in list of vertices.")
raise
sGraph.addEdge(i, j, k)
line = infile.readline()
logging.info("Read graph with " + str(numVertices) + " vertices and " +
str(sGraph.getNumEdges()) + " edges")
return sGraph
def readFromFile(self, fileName):
X = numpy.loadtxt(fileName, skiprows=1, converters=self.converters)
vertexIds = numpy.zeros(X.shape[0] * 2)
#First, we will map the vertex Ids to a set of numbers
for i in range(0, X.shape[0]):
vertexIds[2 * i] = X[i, self.vertex1IdIndex]
vertexIds[2 * i + 1] = X[i, self.vertex2IdIndex]
vertexIds = numpy.unique(vertexIds)
numVertices = vertexIds.shape[0]
numFeatures = len(self.vertex1Indices)
vList = VertexList(numVertices, numFeatures)
sGraph = SparseGraph(vList, self.undirected)
for i in range(0, X.shape[0]):
vertex1Id = X[i, self.vertex1IdIndex]
vertex2Id = X[i, self.vertex2IdIndex]
vertex1 = X[i, self.vertex1Indices]
vertex2 = X[i, self.vertex2Indices]
vertex1VListId = numpy.nonzero(vertexIds == vertex1Id)[0]
vertex2VListId = numpy.nonzero(vertexIds == vertex2Id)[0]
vertex1VListId = int(vertex1VListId)
vertex2VListId = int(vertex2VListId)
vList.setVertex(vertex1VListId, vertex1)
vList.setVertex(vertex2VListId, vertex2)
sGraph.addEdge(vertex1VListId, vertex2VListId, self.edgeWeight)
logging.info("Read " + fileName + " with " +
str(sGraph.getNumVertices()) + " vertices and " +
str(sGraph.getNumEdges()) + " edges")
return sGraph
def testGenerate(self):
#undirected = True
p = 0.0
self.graph.removeAllEdges()
self.erg.setP(p)
graph = self.erg.generate(self.graph)
self.assertEquals(graph.getNumEdges(), 0)
undirected = False
self.graph = SparseGraph(self.vList, undirected)
self.graph.removeAllEdges()
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertEquals(graph.getNumEdges(), 0)
p = 1.0
undirected = True
self.graph = SparseGraph(self.vList, undirected)
self.graph.removeAllEdges()
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertEquals(graph.getNumEdges(), (self.numVertices*self.numVertices-self.numVertices)/2)
p = 1.0
undirected = False
self.graph = SparseGraph(self.vList, undirected)
self.graph.removeAllEdges()
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertEquals(graph.getNumEdges(), self.numVertices*self.numVertices-self.numVertices)
self.assertEquals(graph.getEdge(1, 2), 1)
self.assertEquals(graph.getEdge(1, 1), None)
p = 0.5
numVertices = 1000
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
undirected = False
self.graph = SparseGraph(vList, undirected)
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertAlmostEquals(graph.getNumEdges()/float(numVertices**2 - numVertices), p, places=2)
p = 0.1
self.graph = SparseGraph(vList, undirected)
self.erg = ErdosRenyiGenerator(p)
graph = self.erg.generate(self.graph)
self.assertAlmostEquals(graph.getNumEdges()/float(numVertices**2 - numVertices), p, places=2)
#Test the case in which we have a graph with edges
p = 0.5
numVertices = 10
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList, undirected)
graph.addEdge(0, 1, 5)
graph.addEdge(0, 2)
graph.addEdge(0, 5, 0.7)
graph.addEdge(1, 8)
graph.addEdge(2, 9)
numEdges = graph.getNumEdges()
graph = self.erg.generate(graph, False)
self.assertTrue(graph.getNumEdges() > numEdges)
from pyamg import smoothed_aggregation_solver
from apgl.generator.ErdosRenyiGenerator import ErdosRenyiGenerator
from apgl.graph.SparseGraph import SparseGraph
from apgl.graph.GeneralVertexList import GeneralVertexList
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
numpy.random.seed(21)
p = 0.001
numVertices = 10000
generator = ErdosRenyiGenerator(p)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
print("Num vertices = " + str(graph.getNumVertices()))
print("Num edges = " + str(graph.getNumEdges()))
L = graph.normalisedLaplacianSym(sparse=True)
# L = csr_matrix(L)
print("Created Laplacian")
# ml = smoothed_aggregation_solver(L)
# M = ml.aspreconditioner()
start = time.clock()
w, V = scipy.sparse.linalg.eigsh(L, k=20)
totalTime = time.clock() - start
print(totalTime)
# print(w)
def readFromFile(self, fileName):
X = numpy.loadtxt(fileName, skiprows=1, converters=self.converters)
vertexIds = numpy.zeros(X.shape[0]*2)
#First, we will map the vertex Ids to a set of numbers
for i in range(0, X.shape[0]):
vertexIds[2*i] = X[i, self.vertex1IdIndex]
vertexIds[2*i+1] = X[i, self.vertex2IdIndex]
vertexIds = numpy.unique(vertexIds)
numVertices = vertexIds.shape[0]
numFeatures = len(self.vertex1Indices)
vList = VertexList(numVertices, numFeatures)
sGraph = SparseGraph(vList, self.undirected)
for i in range(0, X.shape[0]):
vertex1Id = X[i, self.vertex1IdIndex]
vertex2Id = X[i, self.vertex2IdIndex]
vertex1 = X[i, self.vertex1Indices]
vertex2 = X[i, self.vertex2Indices]
vertex1VListId = numpy.nonzero(vertexIds==vertex1Id)[0]
vertex2VListId = numpy.nonzero(vertexIds==vertex2Id)[0]
vertex1VListId = int(vertex1VListId)
vertex2VListId = int(vertex2VListId)
vList.setVertex(vertex1VListId, vertex1)
vList.setVertex(vertex2VListId, vertex2)
sGraph.addEdge(vertex1VListId, vertex2VListId, self.edgeWeight)
logging.info("Read " + fileName + " with " + str(sGraph.getNumVertices()) + " vertices and " + str(sGraph.getNumEdges()) + " edges")
return sGraph
def readFromFile(self, fileName):
"""
Read vertices and edges of the graph from the given file name. The file
must have as its first line "Vertices" followed by a list of
vertex indices (one per line). Then the lines following "Arcs" or "Edges"
have a list of pairs of vertex indices represented directed or undirected
edges.
"""
infile = open(fileName, "r")
line = infile.readline()
line = infile.readline()
ind = 0
vertexIdDict = {}
while infile and line != "Edges" and line != "Arcs":
vertexIdDict[int(line)] = ind
line = infile.readline().strip()
ind += 1
if line == "Edges":
undirected = True
elif line == "Arcs":
undirected = False
else:
raise ValueError("Unknown edge types: " + line)
numVertices = len(vertexIdDict)
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
sGraph = SparseGraph(vList, undirected)
line = infile.readline()
while line:
s = line.split()
try:
i = vertexIdDict[int(s[0].strip(',').strip())]
j = vertexIdDict[int(s[1].strip(',').strip())]
k = float(s[2].strip(',').strip())
except KeyError:
print("Vertex not found in list of vertices.")
raise
sGraph.addEdge(i, j, k)
line = infile.readline()
logging.info("Read graph with " + str(numVertices) + " vertices and " + str(sGraph.getNumEdges()) + " edges")
return sGraph
from pyamg import smoothed_aggregation_solver
from apgl.generator.ErdosRenyiGenerator import ErdosRenyiGenerator
from apgl.graph.SparseGraph import SparseGraph
from apgl.graph.GeneralVertexList import GeneralVertexList
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
numpy.random.seed(21)
p = 0.001
numVertices = 10000
generator = ErdosRenyiGenerator(p)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
print("Num vertices = " + str(graph.getNumVertices()))
print("Num edges = " + str(graph.getNumEdges()))
L = graph.normalisedLaplacianSym(sparse=True)
#L = csr_matrix(L)
print("Created Laplacian")
#ml = smoothed_aggregation_solver(L)
#M = ml.aspreconditioner()
start = time.clock()
w, V = scipy.sparse.linalg.eigsh(L, k=20)
totalTime = time.clock() - start
print(totalTime)
#print(w)