def generateStatistics(graph, startDate, endDate, recordStep):
"""
For a given theta, simulate the epidemic, and then return a number of
relevant statistics.
"""
times = []
removedIndices = []
for t in numpy.arange(startDate, endDate+1, recordStep):
times.append(t)
removedIndices.append(graph.removedIndsAt(t))
V = graph.getVertexList().getVertices()
removedArray = numpy.array([len(x) for x in removedIndices])
maleArray = numpy.array([numpy.sum(V[x, HIVVertices.genderIndex]==HIVVertices.male) for x in removedIndices])
femaleArray = numpy.array([numpy.sum(V[x, HIVVertices.genderIndex]==HIVVertices.female) for x in removedIndices])
heteroArray = numpy.array([numpy.sum(V[x, HIVVertices.orientationIndex]==HIVVertices.hetero) for x in removedIndices])
biArray = numpy.array([numpy.sum(V[x, HIVVertices.orientationIndex]==HIVVertices.bi) for x in removedIndices])
randDetectArray = numpy.array([numpy.sum(V[x, HIVVertices.detectionTypeIndex]==HIVVertices.randomDetect) for x in removedIndices])
conDetectArray = numpy.array([numpy.sum(V[x, HIVVertices.detectionTypeIndex]==HIVVertices.contactTrace) for x in removedIndices])
vertexArray = numpy.c_[removedArray, maleArray, femaleArray, heteroArray, biArray, randDetectArray, conDetectArray]
graphStats = GraphStatistics()
removedGraphStats = graphStats.sequenceScalarStats(graph, removedIndices, slowStats=False)
return times, vertexArray, removedGraphStats
def testMeanSeqScalarStats(self):
numFeatures = 1
numVertices = 10
vList = VertexList(numVertices, numFeatures)
p = 0.1
generator = ErdosRenyiGenerator(p)
numGraphs = 50
graphList = []
subgraphIndices = [list(range(3)), list(range(6)), list(range(10))]
for i in range(numGraphs):
graph = generator.generate(SparseGraph(vList))
graphList.append((graph, subgraphIndices))
growthStatistics = GraphStatistics()
meanStats, stdStats = growthStatistics.meanSeqScalarStats(graphList)
#Check some of the stats
for i in range(len(subgraphIndices)):
self.assertEquals(meanStats[i, growthStatistics.numVerticesIndex], len(subgraphIndices[i]))
self.assertEquals(stdStats[i, growthStatistics.numVerticesIndex], 0)
self.assertAlmostEquals(meanStats[i, growthStatistics.numEdgesIndex], 0.5*(len(subgraphIndices[i])*(len(subgraphIndices[i])-1))*p, places=0)
def testScalaraStatistics(self):
numVertices = 10
graph = DictGraph(numVertices)
graph.addEdge(0, 1)
growthStatistics = GraphStatistics()
statsArray = growthStatistics.scalarStatistics(graph, False)
def testSequenceVectorStats(self):
numFeatures = 1
numVertices = 10
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
graph.addEdge(0, 2)
graph.addEdge(0, 1)
subgraphIndices = [[0, 1, 3], [0, 1, 2, 3]]
growthStatistics = GraphStatistics()
statsList = growthStatistics.sequenceVectorStats(graph, subgraphIndices)
def testSequenceScalarStats(self):
numFeatures = 1
numVertices = 10
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
graph.addEdge(0, 2)
graph.addEdge(0, 1)
subgraphIndices = [[0, 1, 3], [0, 1, 2, 3]]
growthStatistics = GraphStatistics()
statsArray = growthStatistics.sequenceScalarStats(graph, subgraphIndices)
self.assertTrue(statsArray[0, 0] == 3.0)
self.assertTrue(statsArray[1, 0] == 4.0)
self.assertTrue(statsArray[0, 1] == 1.0)
self.assertTrue(statsArray[1, 1] == 2.0)
def generateStatistics(graph, times):
"""
For a given theta, simulate the epidemic, and then return a number of
relevant statistics.
"""
contactIndices = []
removedIndices = []
infectedIndices = []
for t in times:
removedIndices.append(graph.removedIndsAt(t))
infectedIndices.append(graph.infectedIndsAt(t))
contactIndices.append(graph.contactIndsAt(t))
infectedIndices = numpy.array(infectedIndices)
V = graph.getVertexList().getVertices()
graphStatsList = []
for inds in [contactIndices, removedIndices]:
numVerticesArray = numpy.array([len(x) for x in inds])
maleArray = numpy.array([numpy.sum(V[x, HIVVertices.genderIndex]==HIVVertices.male) for x in inds])
femaleArray = numpy.array([numpy.sum(V[x, HIVVertices.genderIndex]==HIVVertices.female) for x in inds])
heteroArray = numpy.array([numpy.sum(V[x, HIVVertices.orientationIndex]==HIVVertices.hetero) for x in inds])
biArray = numpy.array([numpy.sum(V[x, HIVVertices.orientationIndex]==HIVVertices.bi) for x in inds])
randDetectArray = numpy.array([numpy.sum(V[x, HIVVertices.detectionTypeIndex]==HIVVertices.randomDetect) for x in inds])
conDetectArray = numpy.array([numpy.sum(V[x, HIVVertices.detectionTypeIndex]==HIVVertices.contactTrace) for x in inds])
vertexArray = numpy.c_[numVerticesArray, maleArray, femaleArray, heteroArray, biArray, randDetectArray, conDetectArray]
graphStatistics = GraphStatistics()
graphStats = graphStatistics.sequenceScalarStats(graph, inds, slowStats=False)
graphStatsList.append(graphStats)
finalRemovedGraph = graph.subgraph(removedIndices[-1])
finalRemovedDegrees = finalRemovedGraph.degreeDistribution()
return vertexArray, infectedIndices, removedIndices, graphStatsList[0], graphStatsList[1], finalRemovedDegrees
def testScalarStatistics(self):
numFeatures = 1
numVertices = 10
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
graph.addEdge(0, 1)
growthStatistics = GraphStatistics()
statsArray = growthStatistics.scalarStatistics(graph)
#logging.debug(statsArray)
self.assertTrue(statsArray[growthStatistics.numVerticesIndex] == 10.0)
self.assertTrue(statsArray[growthStatistics.numEdgesIndex] == 1.0)
self.assertTrue(statsArray[growthStatistics.maxComponentSizeIndex] == 2.0)
self.assertTrue(statsArray[growthStatistics.maxComponentEdgesIndex] == 1.0)
self.assertTrue(statsArray[growthStatistics.numComponentsIndex] == 9.0)
self.assertEquals(statsArray[growthStatistics.meanComponentSizeIndex], 10.0/9.0)
self.assertTrue(statsArray[growthStatistics.meanDegreeIndex] == 0.2)
self.assertTrue(statsArray[growthStatistics.diameterIndex] == 1.0)
self.assertTrue(statsArray[growthStatistics.effectiveDiameterIndex] == 1.0)
self.assertTrue(statsArray[growthStatistics.densityIndex] == 1.0/45)
self.assertEquals(statsArray[growthStatistics.geodesicDistanceIndex], 1.0/55)
self.assertEquals(statsArray[growthStatistics.harmonicGeoDistanceIndex], 55.0)
self.assertEquals(statsArray[growthStatistics.geodesicDistMaxCompIndex], 1.0/3)
self.assertEquals(statsArray[growthStatistics.numNonSingletonComponentsIndex], 1.0)
self.assertEquals(statsArray[growthStatistics.numTriOrMoreComponentsIndex], 0.0)
self.assertEquals(statsArray[growthStatistics.secondComponentSizeIndex], 1.0)
self.assertEquals(statsArray[growthStatistics.maxCompMeanDegreeIndex], 1.0)
graph.addEdge(0, 2)
graph.addEdge(3,4)
graph.addEdge(3,5)
graph.addEdge(3,6)
graph.addEdge(7,8)
statsArray = growthStatistics.scalarStatistics(graph)
self.assertEquals(statsArray[growthStatistics.numNonSingletonComponentsIndex], 3.0)
self.assertEquals(statsArray[growthStatistics.numTriOrMoreComponentsIndex], 2.0)
self.assertEquals(statsArray[growthStatistics.secondComponentSizeIndex], 3.0)
self.assertEquals(statsArray[growthStatistics.maxCompMeanDegreeIndex], 1.5)
#Test on a directed graph
graph = SparseGraph(vList, False)
graph.addEdge(0, 1)
statsArray = growthStatistics.scalarStatistics(graph, treeStats=True)
self.assertTrue(statsArray[growthStatistics.numVerticesIndex] == 10.0)
self.assertTrue(statsArray[growthStatistics.numEdgesIndex] == 1.0)
self.assertTrue(statsArray[growthStatistics.maxComponentSizeIndex] == -1)
self.assertTrue(statsArray[growthStatistics.maxComponentEdgesIndex] == -1)
self.assertTrue(statsArray[growthStatistics.numComponentsIndex] == -1)
self.assertTrue(statsArray[growthStatistics.meanComponentSizeIndex] == -1)
self.assertEquals(statsArray[growthStatistics.meanDegreeIndex], 0.1)
self.assertTrue(statsArray[growthStatistics.diameterIndex] == 1.0)
self.assertTrue(statsArray[growthStatistics.effectiveDiameterIndex] == 1.0)
self.assertTrue(statsArray[growthStatistics.densityIndex] == 1.0/90)
self.assertEquals(statsArray[growthStatistics.geodesicDistanceIndex], 1.0/100)
self.assertEquals(statsArray[growthStatistics.harmonicGeoDistanceIndex], 100)
self.assertEquals(statsArray[growthStatistics.meanTreeSizeIndex], 10.0/9)
self.assertEquals(statsArray[growthStatistics.meanTreeDepthIndex], 1.0/9)
self.assertEquals(statsArray[growthStatistics.maxTreeSizeIndex], 2.0)
self.assertEquals(statsArray[growthStatistics.maxTreeDepthIndex], 1.0)
self.assertEquals(statsArray[growthStatistics.numTreesIndex], 9.0)
self.assertEquals(statsArray[growthStatistics.numNonSingletonTreesIndex], 1.0)
#Test that the max tree is decribed correctly
graph.addEdge(2, 3)
graph.addEdge(2, 4)
graph.addEdge(3, 5)
graph.addEdge(3, 6)
statsArray = growthStatistics.scalarStatistics(graph, treeStats=True)
self.assertEquals(statsArray[growthStatistics.maxTreeSizeIndex], 5.0)
self.assertEquals(statsArray[growthStatistics.maxTreeDepthIndex], 2.0)
self.assertEquals(statsArray[growthStatistics.secondTreeSizeIndex], 2.0)
self.assertEquals(statsArray[growthStatistics.secondTreeDepthIndex], 1.0)
self.assertEquals(statsArray[growthStatistics.numTreesIndex], 5.0)
self.assertEquals(statsArray[growthStatistics.numNonSingletonTreesIndex], 2.0)
#Try a zero size graph
numFeatures = 0
numVertices = 0
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
statsArray = growthStatistics.scalarStatistics(graph)
self.assertEquals(statsArray[growthStatistics.numVerticesIndex], 0)
self.assertEquals(statsArray[growthStatistics.numEdgesIndex], 0)
self.assertEquals(statsArray[growthStatistics.maxComponentSizeIndex], 0)
self.assertTrue(statsArray[growthStatistics.maxComponentEdgesIndex] == 0)
self.assertEquals(statsArray[growthStatistics.numComponentsIndex], 0)
self.assertEquals(statsArray[growthStatistics.meanComponentSizeIndex], 0)
self.assertEquals(statsArray[growthStatistics.meanDegreeIndex], 0)
self.assertEquals(statsArray[growthStatistics.diameterIndex], 0)
self.assertEquals(statsArray[growthStatistics.effectiveDiameterIndex], 0)
self.assertEquals(statsArray[growthStatistics.densityIndex], 0)
self.assertEquals(statsArray[growthStatistics.geodesicDistanceIndex], 0.0)
self.assertEquals(statsArray[growthStatistics.harmonicGeoDistanceIndex], 0.0)
self.assertEquals(statsArray[growthStatistics.geodesicDistMaxCompIndex], 0.0)
self.assertEquals(statsArray[growthStatistics.numNonSingletonComponentsIndex], 0.0)
graph = SparseGraph(vList, False)
statsArray = growthStatistics.scalarStatistics(graph)
self.assertEquals(statsArray[growthStatistics.numVerticesIndex], 0)
self.assertEquals(statsArray[growthStatistics.numEdgesIndex], 0)
self.assertEquals(statsArray[growthStatistics.maxComponentSizeIndex], -1)
self.assertEquals(statsArray[growthStatistics.numComponentsIndex], -1)
self.assertEquals(statsArray[growthStatistics.meanComponentSizeIndex], -1)
self.assertEquals(statsArray[growthStatistics.maxComponentEdgesIndex], -1)
self.assertEquals(statsArray[growthStatistics.meanDegreeIndex], 0)
self.assertEquals(statsArray[growthStatistics.diameterIndex], 0)
self.assertEquals(statsArray[growthStatistics.effectiveDiameterIndex], 0)
self.assertEquals(statsArray[growthStatistics.densityIndex], 0)
self.assertEquals(statsArray[growthStatistics.geodesicDistanceIndex], 0.0)
self.assertEquals(statsArray[growthStatistics.harmonicGeoDistanceIndex], 0.0)
self.assertEquals(statsArray[growthStatistics.geodesicDistMaxCompIndex], -1)
self.assertEquals(statsArray[growthStatistics.meanTreeSizeIndex], -1)
self.assertEquals(statsArray[growthStatistics.meanTreeDepthIndex], -1)
self.assertEquals(statsArray[growthStatistics.numNonSingletonComponentsIndex], -1)
self.assertEquals(statsArray[growthStatistics.numTreesIndex], -1)
self.assertEquals(statsArray[growthStatistics.numNonSingletonTreesIndex], -1)
def testVectorStatistics(self):
numFeatures = 1
numVertices = 10
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
graph.addEdge(0, 2)
graph.addEdge(0, 1)
growthStatistics = GraphStatistics()
statsDict = growthStatistics.vectorStatistics(graph)
self.assertTrue((statsDict["outDegreeDist"] == numpy.array([7,2,1])).all())
self.assertTrue((statsDict["inDegreeDist"] == numpy.array([7,2,1])).all())
self.assertTrue((statsDict["hopCount"] == numpy.array([10,14,16])).all())
self.assertTrue((statsDict["triangleDist"] == numpy.array([10])).all())
W = graph.getWeightMatrix()
W = (W + W.T)/2
lmbda, V = numpy.linalg.eig(W)
maxEigVector = V[:, numpy.argmax(lmbda)]
lmbda = numpy.flipud(numpy.sort(lmbda[lmbda>0]))
self.assertTrue((statsDict["maxEigVector"] == maxEigVector).all())
self.assertTrue((statsDict["eigenDist"] == lmbda).all())
self.assertTrue((statsDict["componentsDist"] == numpy.array([0, 7, 0, 1])).all())
graph.addEdge(0, 3)
graph.addEdge(0, 4)
graph.addEdge(1, 4)
growthStatistics = GraphStatistics()
statsDict = growthStatistics.vectorStatistics(graph)
self.assertTrue((statsDict["outDegreeDist"] == numpy.array([5,2,2,0,1])).all())
self.assertTrue((statsDict["inDegreeDist"] == numpy.array([5,2,2,0,1])).all())
self.assertTrue((statsDict["hopCount"] == numpy.array([10,20,30])).all())
self.assertTrue((statsDict["triangleDist"] == numpy.array([7, 0, 3])).all())
W = graph.getWeightMatrix()
W = (W + W.T)/2
lmbda, V = numpy.linalg.eig(W)
maxEigVector = V[:, numpy.argmax(lmbda)]
lmbda = numpy.flipud(numpy.sort(lmbda[lmbda>0]))
self.assertTrue((statsDict["maxEigVector"] == maxEigVector).all())
self.assertTrue((statsDict["eigenDist"] == lmbda).all())
self.assertTrue((statsDict["componentsDist"] == numpy.array([0, 5, 0, 0, 0, 1])).all())
#Test on a directed graph and generating tree statistics
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList, False)
graph.addEdge(0, 1)
graph.addEdge(0, 2)
graph.addEdge(2, 3)
graph.addEdge(4, 5)
statsDict = growthStatistics.vectorStatistics(graph, treeStats=True)
self.assertTrue(( statsDict["inDegreeDist"] == numpy.array([6, 4]) ).all())
self.assertTrue(( statsDict["outDegreeDist"] == numpy.array([7, 2, 1]) ).all())
self.assertTrue(( statsDict["triangleDist"] == numpy.array([10]) ).all())
self.assertTrue(( statsDict["treeSizesDist"] == numpy.array([0, 4, 1, 0, 1]) ).all())
self.assertTrue(( statsDict["treeDepthsDist"] == numpy.array([4, 1, 1]) ).all())