def testWriteToFile3(self):
"""
We will test out writing out some random graphs to Pajek
"""
numVertices = 20
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
pw = PajekWriter()
directory = PathDefaults.getOutputDir() + "test/"
pw.writeToFile(directory + "erdosRenyi20", graph)
#Now write a small world graph
p = 0.2
k = 3
graph.removeAllEdges()
generator = SmallWorldGenerator(p, k)
graph = generator.generate(graph)
pw.writeToFile(directory + "smallWorld20", graph)
def testWriteToFile3(self):
"""
We will test out writing out some random graphs to Pajek
"""
numVertices = 20
numFeatures = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
pw = PajekWriter()
directory = PathDefaults.getOutputDir() + "test/"
pw.writeToFile(directory + "erdosRenyi20", graph)
#Now write a small world graph
p = 0.2
k = 3
graph.removeAllEdges()
generator = SmallWorldGenerator(p, k)
graph = generator.generate(graph)
pw.writeToFile(directory + "smallWorld20", graph)
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 testLearnModel(self):
numVertices = 100
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
vertexIndices = list(range(0, numVertices))
k = 2
learner = GrowthLearner(k)
tol = 10**-1
#Lets test the values of alpha on a series of Erdos-Renyi graphs
for i in range(1, 6):
p = float(i)/10
graph.removeAllEdges()
graph = generator.generate(graph)
alpha = learner.learnModel(graph, vertexIndices)
logging.debug((numpy.linalg.norm(alpha - numpy.array([p, 0]))))
#self.assertTrue(numpy.linalg.norm(alpha - numpy.array([p, 0])) < tol)
#Now test the learning on some preferencial attachment graphs
ell = 10
m = 8
vertexIndices = list(range(ell, numVertices))
graph.removeAllEdges()
generator = BarabasiAlbertGenerator(ell, m)
graph = generator.generate(graph)
alpha = learner.learnModel(graph, vertexIndices)
logging.debug(alpha)
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 testGenerate(self):
degSequence = numpy.array([2, 1, 3, 0, 0, 0, 0, 0, 0, 1])
generator = ConfigModelGenerator(degSequence)
numVertices = 10
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
tol = 3
self.assertTrue(
numpy.linalg.norm(degSequence - graph.degreeSequence()) < tol)
degSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1])
generator.setOutDegSequence(degSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
self.assertTrue(
numpy.linalg.norm(degSequence - graph.degreeSequence()) < tol)
#Test using a non-empty graph
degSequence = numpy.array([0, 0, 0, 2, 0, 0, 0, 1, 1, 0])
generator.setOutDegSequence(degSequence)
oldDegSequence = graph.degreeSequence()
self.assertRaises(ValueError, generator.generate, graph, True)
graph = generator.generate(graph, False)
diffSequence = graph.degreeSequence() - oldDegSequence
self.assertTrue(numpy.linalg.norm(degSequence - diffSequence) < tol)
#Test the case where we also have an in-degree sequence
degSequence = numpy.array([2, 1, 3, 0, 0, 0, 0, 0, 0, 1])
inDegSequence = numpy.array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0])
generator = ConfigModelGenerator(degSequence, inDegSequence)
graph = SparseGraph(GeneralVertexList(numVertices))
self.assertRaises(ValueError, generator.generate, graph)
graph = SparseGraph(GeneralVertexList(numVertices), False)
graph = generator.generate(graph)
self.assertTrue(
numpy.linalg.norm(degSequence - graph.outDegreeSequence()) < tol)
self.assertTrue(
numpy.linalg.norm(inDegSequence - graph.inDegreeSequence()) < tol)
outDegSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1])
inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 2, 1])
generator.setOutDegSequence(outDegSequence)
generator.setInDegSequence(inDegSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
self.assertTrue(
numpy.linalg.norm(outDegSequence -
graph.outDegreeSequence()) < tol)
self.assertTrue(
numpy.linalg.norm(inDegSequence - graph.inDegreeSequence()) < tol)
#In the case that the in-degree sequence sum larger than that of the out-degree it is
#not satisfied, but the out-degree should be.
inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 5, 6])
generator.setInDegSequence(inDegSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
self.assertTrue(
numpy.linalg.norm(outDegSequence -
graph.outDegreeSequence()) < tol)
#Now try the other way around
generator.setOutDegSequence(inDegSequence)
generator.setInDegSequence(outDegSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
self.assertTrue(
numpy.linalg.norm(outDegSequence - graph.inDegreeSequence()) < tol)
#Test growing graph
outDegSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1])
inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 2, 1])
generator.setOutDegSequence(outDegSequence)
generator.setInDegSequence(inDegSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
newOutDegreeSequence = numpy.array([2, 1, 3, 5, 2, 1, 4, 0, 0, 1])
newInDegreeSequence = numpy.array([2, 3, 2, 2, 3, 1, 2, 1, 2, 1])
diffOutSequence = newOutDegreeSequence - graph.outDegreeSequence()
diffInSequence = newInDegreeSequence - graph.inDegreeSequence()
generator.setOutDegSequence(diffOutSequence)
generator.setInDegSequence(diffInSequence)
graph = generator.generate(graph, False)
self.assertTrue(
numpy.linalg.norm(newOutDegreeSequence -
graph.outDegreeSequence()) < tol)
self.assertTrue(
numpy.linalg.norm(newInDegreeSequence -
graph.inDegreeSequence()) < tol)
class ErdoRenyiGeneratorTest(unittest.TestCase):
def setUp(self):
numpy.set_printoptions(suppress=True, linewidth=200, precision=5)
self.numVertices = 10;
self.numFeatures = 2;
self.vList = VertexList(self.numVertices, self.numFeatures)
self.graph = SparseGraph(self.vList)
self.p = 0.1
self.erg = ErdosRenyiGenerator(self.p)
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)
@apgl.skip("")
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([])
#matplotlib.pyplot.show()
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 testErdosRenyiGenerations(self):
numVertices = 20
graph = DenseGraph(GeneralVertexList(numVertices))
p = 0.2
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
def testGenerate(self):
degSequence = numpy.array([2, 1, 3, 0, 0, 0, 0, 0, 0, 1])
generator = ConfigModelGenerator(degSequence)
numVertices = 10
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
tol = 3
self.assertTrue(numpy.linalg.norm(degSequence - graph.degreeSequence()) < tol)
degSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1])
generator.setOutDegSequence(degSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
self.assertTrue(numpy.linalg.norm(degSequence - graph.degreeSequence()) < tol)
#Test using a non-empty graph
degSequence = numpy.array([0, 0, 0, 2, 0, 0, 0, 1, 1, 0])
generator.setOutDegSequence(degSequence)
oldDegSequence = graph.degreeSequence()
self.assertRaises(ValueError, generator.generate, graph, True)
graph = generator.generate(graph, False)
diffSequence = graph.degreeSequence() - oldDegSequence
self.assertTrue(numpy.linalg.norm(degSequence - diffSequence) < tol)
#Test the case where we also have an in-degree sequence
degSequence = numpy.array([2, 1, 3, 0, 0, 0, 0, 0, 0, 1])
inDegSequence = numpy.array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0])
generator = ConfigModelGenerator(degSequence, inDegSequence)
graph = SparseGraph(GeneralVertexList(numVertices))
self.assertRaises(ValueError, generator.generate, graph)
graph = SparseGraph(GeneralVertexList(numVertices), False)
graph = generator.generate(graph)
self.assertTrue(numpy.linalg.norm(degSequence - graph.outDegreeSequence()) < tol)
self.assertTrue(numpy.linalg.norm(inDegSequence - graph.inDegreeSequence()) < tol)
outDegSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1])
inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 2, 1])
generator.setOutDegSequence(outDegSequence)
generator.setInDegSequence(inDegSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
self.assertTrue(numpy.linalg.norm(outDegSequence - graph.outDegreeSequence()) < tol)
self.assertTrue(numpy.linalg.norm(inDegSequence - graph.inDegreeSequence()) < tol)
#In the case that the in-degree sequence sum larger than that of the out-degree it is
#not satisfied, but the out-degree should be.
inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 5, 6])
generator.setInDegSequence(inDegSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
self.assertTrue(numpy.linalg.norm(outDegSequence - graph.outDegreeSequence()) < tol)
#Now try the other way around
generator.setOutDegSequence(inDegSequence)
generator.setInDegSequence(outDegSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
self.assertTrue(numpy.linalg.norm(outDegSequence - graph.inDegreeSequence()) < tol)
#Test growing graph
outDegSequence = numpy.array([2, 1, 3, 0, 2, 1, 4, 0, 0, 1])
inDegSequence = numpy.array([1, 2, 1, 1, 2, 1, 2, 1, 2, 1])
generator.setOutDegSequence(outDegSequence)
generator.setInDegSequence(inDegSequence)
graph.removeAllEdges()
graph = generator.generate(graph)
newOutDegreeSequence = numpy.array([2, 1, 3, 5, 2, 1, 4, 0, 0, 1])
newInDegreeSequence = numpy.array([2, 3, 2, 2, 3, 1, 2, 1, 2, 1])
diffOutSequence = newOutDegreeSequence - graph.outDegreeSequence()
diffInSequence = newInDegreeSequence - graph.inDegreeSequence()
generator.setOutDegSequence(diffOutSequence)
generator.setInDegSequence(diffInSequence)
graph = generator.generate(graph, False)
self.assertTrue(numpy.linalg.norm(newOutDegreeSequence - graph.outDegreeSequence()) < tol)
self.assertTrue(numpy.linalg.norm(newInDegreeSequence - graph.inDegreeSequence()) < tol)
