def createModel(targetGraph, startDate, endDate, recordStep, M, matchAlpha, breakSize, matchAlg, theta=None):
alpha = 2
zeroVal = 0.9
numpy.random.seed(21)
graph = targetGraph.subgraph(targetGraph.removedIndsAt(startDate))
graph.addVertices(M-graph.size)
logging.debug("Created graph: " + str(graph))
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
featureInds = numpy.ones(graph.vlist.getNumFeatures(), numpy.bool)
featureInds[HIVVertices.dobIndex] = False
featureInds[HIVVertices.infectionTimeIndex] = False
featureInds[HIVVertices.hiddenDegreeIndex] = False
featureInds[HIVVertices.stateIndex] = False
featureInds = numpy.arange(featureInds.shape[0])[featureInds]
matcher = GraphMatch(matchAlg, alpha=matchAlpha, featureInds=featureInds, useWeightM=False)
graphMetrics = HIVGraphMetrics2(targetGraph, breakSize, matcher, startDate)
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
if theta != None:
model.setParams(theta)
return model
def testSimulate2(self):
alpha = 2
zeroVal = 0.9
startDate = 0.0
endDate = 200.0
M = 1000
undirected = True
theta, sigmaTheta, pertTheta = HIVModelUtils.toyTheta()
numpy.random.seed(21)
graph = HIVGraph(M, undirected)
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate, metrics=None)
#model.setRecordStep(recordStep)
model.setParams(theta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
numVertices = graph.size
numEdges = graph.getNumEdges()
#Try again
numpy.random.seed(21)
graph = HIVGraph(M, undirected)
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate, metrics=None)
model.setParams(theta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
numVertices2 = graph.size
numEdges2 = graph.getNumEdges()
self.assertEquals(numVertices2, numVertices)
self.assertEquals(numEdges2, numEdges)
def simulate(theta, startDate, endDate, recordStep, M, graphMetrics=None):
undirected = True
graph = HIVGraph(M, undirected)
logging.debug("Created graph: " + str(graph))
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate, metrics=graphMetrics)
model.setRecordStep(recordStep)
model.setParams(theta)
logging.debug("Theta = " + str(theta))
return model.simulate(True)
def runModel(theta, endDate=100.0, M=1000):
numpy.random.seed(21)
undirected= True
recordStep = 10
startDate = 0
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
graph = HIVGraph(M, undirected)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
logging.debug("MeanTheta=" + str(theta))
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate)
model.setRecordStep(recordStep)
model.setParams(theta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
return times, infectedIndices, removedIndices, graph, model
def testSimulate2(self):
startDate = 0.0
endDate = 100.0
M = 1000
meanTheta, sigmaTheta = HIVModelUtils.estimatedRealTheta()
undirected = True
graph = HIVGraph(M, undirected)
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
meanTheta[4] = 0.1
recordStep = 10
printStep = 10
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate)
model.setRecordStep(recordStep)
model.setPrintStep(printStep)
model.setParams(meanTheta)
initialInfected = graph.getInfectedSet()
times, infectedIndices, removedIndices, graph = model.simulate(True)
#Now test the final graph
edges = graph.getAllEdges()
for i, j in edges:
if graph.vlist.V[i, HIVVertices.genderIndex] == graph.vlist.V[j, HIVVertices.genderIndex] and (graph.vlist.V[i, HIVVertices.orientationIndex] != HIVVertices.bi or graph.vlist.V[j, HIVVertices.orientationIndex] != HIVVertices.bi):
self.fail()
finalInfected = graph.getInfectedSet()
finalRemoved = graph.getRemovedSet()
self.assertEquals(numpy.intersect1d(initialInfected, finalRemoved).shape[0], len(initialInfected))
#Test case where there is no contact
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, 0, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
self.assertEquals(len(graph.getInfectedSet()), 100)
self.assertEquals(len(graph.getRemovedSet()), 0)
self.assertEquals(graph.getNumEdges(), 0)
heteroContactRate = 0.1
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
self.assertEquals(len(graph.getInfectedSet()), 100)
self.assertEquals(len(graph.getRemovedSet()), 0)
edges = graph.getAllEdges()
for i, j in edges:
self.assertNotEqual(graph.vlist.V[i, HIVVertices.genderIndex], graph.vlist.V[j, HIVVertices.genderIndex])
#Number of conacts = rate*people*time
infectedSet = graph.getInfectedSet()
numHetero = (graph.vlist.V[list(infectedSet), HIVVertices.orientationIndex] == HIVVertices.hetero).sum()
self.assertTrue(abs(numHetero*endDate*heteroContactRate- model.getNumContacts()) < 100)
heteroContactRate = 0.01
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
infectedSet = graph.getInfectedSet()
numHetero = (graph.vlist.V[list(infectedSet), HIVVertices.orientationIndex] == HIVVertices.hetero).sum()
self.assertAlmostEqual(numHetero*endDate*heteroContactRate/100, model.getNumContacts()/100.0, 0)
