def testInit(self):
degSequence = numpy.array([2, 1, 2])
generator = ConfigModelGenerator(degSequence)
generator = ConfigModelGenerator(degSequence, degSequence)
degSequence = numpy.array([2, 1, 3])
generator = ConfigModelGenerator(degSequence)
generator = ConfigModelGenerator(degSequence, degSequence)
self.assertRaises(ValueError, ConfigModelGenerator, None)
degSequence2 = numpy.array([2, 1, 2.2])
self.assertRaises(ValueError, ConfigModelGenerator, degSequence2)
self.assertRaises(ValueError, ConfigModelGenerator, degSequence,
degSequence2)
degSequence2 = numpy.array([2, 1, -1])
self.assertRaises(ValueError, ConfigModelGenerator, degSequence2)
self.assertRaises(ValueError, ConfigModelGenerator, degSequence,
degSequence2)
degSequence2 = numpy.array([2, 1, 4])
self.assertRaises(ValueError, ConfigModelGenerator, degSequence2)
self.assertRaises(ValueError, ConfigModelGenerator, degSequence,
degSequence2)
def computeContactConfigGraphs():
graphFileNameBase = resultsDir + "ConfigGraph"
for j in range(numGraphs):
configGraph = SparseGraph(GeneralVertexList(numVertices))
degSequence = numpy.zeros(numVertices, numpy.int)
lastDegSequence = numpy.zeros(numVertices, numpy.int)
generator = ConfigModelGenerator(lastDegSequence)
for i in dayList:
logging.info("Date: " + str(DateUtils.getDateStrFromDay(i, startYear)))
subgraphIndices = numpy.nonzero(detections <= i)[0]
subgraphIndices = numpy.unique(subgraphIndices)
subgraph = sGraph.subgraph(subgraphIndices)
subDegSequence = subgraph.degreeSequence()
degSequence[subgraphIndices] = subDegSequence
diffSequence = degSequence - lastDegSequence
generator.setOutDegSequence(diffSequence)
configGraph = generator.generate(configGraph, False)
lastDegSequence = configGraph.degreeSequence()
assert (degSequence>=lastDegSequence).all()
assert subgraph.getNumEdges() >= configGraph.getNumEdges()
configGraph.save(graphFileNameBase + str(j))
def ConfigurationModel(edges_list):
deg_dict = defaultdict(int)
for u, v in edges_list:
deg_dict[v] += 1
l = array(deg_dict.values())
n = len(l)
graph = SparseGraph(n)
generator = ConfigModelGenerator(l)
graph = generator.generate(graph)
l, _ = graph.adjacencyList()
return convertAdjListToEdgeList(l)
def ConfigurationModel(edges_list):
deg_dict = defaultdict(int)
for u,v in edges_list:
deg_dict[v] += 1
l = array(deg_dict.values())
n = len(l)
graph = SparseGraph(n)
generator = ConfigModelGenerator(l)
graph = generator.generate(graph)
l, _ = graph.adjacencyList()
return convertAdjListToEdgeList(l)
def testGenerate2(self):
"""
Make sure that the generated degree is less than or equal to the given degree
"""
numVertices = 10
for i in range(10):
degSequence = numpy.random.randint(0, 3, numVertices)
generator = ConfigModelGenerator(degSequence)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
self.assertTrue((graph.outDegreeSequence()<=degSequence).all())
#We try to match an evolving degree sequence
degSequence1 = numpy.array([0,0,1,1,1,2,2,2,3, 4])
degSequence2 = numpy.array([2,0,3,1,2,2,2,2,3, 4])
degSequence3 = numpy.array([2,1,4,1,2,2,2,2,3, 6])
generator = ConfigModelGenerator(degSequence1)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
self.assertTrue((degSequence1>= graph.outDegreeSequence()).all())
deltaSequence = degSequence2 - graph.outDegreeSequence()
generator = ConfigModelGenerator(deltaSequence)
graph = generator.generate(graph, False)
self.assertTrue((degSequence2>= graph.outDegreeSequence()).all())
deltaSequence = degSequence3 - graph.outDegreeSequence()
generator = ConfigModelGenerator(deltaSequence)
graph = generator.generate(graph, False)
self.assertTrue((degSequence3>= graph.outDegreeSequence()).all())
def computeInfectConfigGraphs():
#We need the directed infection graph
hivReader = HIVGraphReader()
graph = hivReader.readHIVGraph(False)
sGraphInfect = graph.getSparseGraph(edgeTypeIndex2)
sGraph = sGraphInfect
graphFileNameBase = resultsDir + "ConfigInfectGraph"
for j in range(numGraphs):
configGraph = SparseGraph(GeneralVertexList(numVertices), False)
outDegSequence = numpy.zeros(numVertices, numpy.int)
inDegSequence = numpy.zeros(numVertices, numpy.int)
lastOutDegSequence = numpy.zeros(numVertices, numpy.int)
lastInDegSequence = numpy.zeros(numVertices, numpy.int)
generator = ConfigModelGenerator(lastOutDegSequence, lastInDegSequence)
for i in dayList:
logging.info("Date: " + str(DateUtils.getDateStrFromDay(i, startYear)))
subgraphIndices = numpy.nonzero(detections <= i)[0]
subgraphIndices = numpy.unique(subgraphIndices)
subgraph = sGraph.subgraph(subgraphIndices)
outDegSequence[subgraphIndices] = subgraph.outDegreeSequence()
inDegSequence[subgraphIndices] = subgraph.inDegreeSequence()
outDiffSequence = outDegSequence - lastOutDegSequence
inDiffSequence = inDegSequence - lastInDegSequence
generator.setInDegSequence(inDiffSequence)
generator.setOutDegSequence(outDiffSequence)
configGraph = generator.generate(configGraph, False)
lastOutDegSequence = configGraph.outDegreeSequence()
lastInDegSequence = configGraph.inDegreeSequence()
assert (outDegSequence>=lastOutDegSequence).all()
assert (inDegSequence>=lastInDegSequence).all()
configGraph.save(graphFileNameBase + str(j))
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)
def testGenerate2(self):
"""
Make sure that the generated degree is less than or equal to the given degree
"""
numVertices = 10
for i in range(10):
degSequence = numpy.random.randint(0, 3, numVertices)
generator = ConfigModelGenerator(degSequence)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
self.assertTrue((graph.outDegreeSequence() <= degSequence).all())
#We try to match an evolving degree sequence
degSequence1 = numpy.array([0, 0, 1, 1, 1, 2, 2, 2, 3, 4])
degSequence2 = numpy.array([2, 0, 3, 1, 2, 2, 2, 2, 3, 4])
degSequence3 = numpy.array([2, 1, 4, 1, 2, 2, 2, 2, 3, 6])
generator = ConfigModelGenerator(degSequence1)
graph = SparseGraph(GeneralVertexList(numVertices))
graph = generator.generate(graph)
self.assertTrue((degSequence1 >= graph.outDegreeSequence()).all())
deltaSequence = degSequence2 - graph.outDegreeSequence()
generator = ConfigModelGenerator(deltaSequence)
graph = generator.generate(graph, False)
self.assertTrue((degSequence2 >= graph.outDegreeSequence()).all())
deltaSequence = degSequence3 - graph.outDegreeSequence()
generator = ConfigModelGenerator(deltaSequence)
graph = generator.generate(graph, False)
self.assertTrue((degSequence3 >= graph.outDegreeSequence()).all())
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)
