def profileSimulate(self):
startDate, endDate, recordStep, printStep, M, targetGraph = HIVModelUtils.realSimulationParams()
meanTheta, sigmaTheta = HIVModelUtils.estimatedRealTheta()
meanTheta = numpy.array([337, 1.4319, 0.211, 0.0048, 0.0032, 0.5229, 0.042, 0.0281, 0.0076, 0.0293])
undirected = True
graph = HIVGraph(M, undirected)
logging.info("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)
model.setT0(startDate)
model.setT(startDate+100)
model.setRecordStep(recordStep)
model.setPrintStep(printStep)
model.setParams(meanTheta)
logging.debug("MeanTheta=" + str(meanTheta))
ProfileUtils.profile('model.simulate()', globals(), locals())
graphMetrics.breakDist = 0.0
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
return model
if len(sys.argv) > 1:
numProcesses = int(sys.argv[1])
else:
numProcesses = multiprocessing.cpu_count()
purtScale = 0.02
meanTheta, sigmaTheta = HIVModelUtils.estimatedRealTheta()
abcParams = HIVABCParameters(meanTheta, sigmaTheta, purtScale)
thetaDir = resultsDir + "theta/"
abcSMC = ABCSMC(epsilonArray, createModel, abcParams, thetaDir, True)
abcSMC.setPosteriorSampleSize(posteriorSampleSize)
abcSMC.batchSize = 50
abcSMC.maxRuns = 2000
thetasArray = abcSMC.run()
meanTheta = numpy.mean(thetasArray, 0)
stdTheta = numpy.std(thetasArray, 0)
logging.debug(thetasArray)
logging.debug("meanTheta=" + str(meanTheta))
logging.debug("stdTheta=" + str(stdTheta))
logging.debug("realTheta=" + str(HIVModelUtils.toyTheta()[0]))
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)
