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 simulateModel(theta):
"""
The parameter t is the particle index.
"""
logging.debug("theta=" + str(theta))
#We start with the observed graph at the start date
graph = targetGraph.subgraph(targetGraph.removedIndsAt(startDate))
graph.addVertices(M-graph.size)
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, float(endDate))
recordStep = (endDate-startDate)/float(numRecordSteps)
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
model.setParams(theta)
model.simulate()
objective = model.objective()
return objective
def runModel(meanTheta):
startDate, endDate, recordStep, M, targetGraph = HIVModelUtils.toySimulationParams()
endDate = 1000.0
recordStep = 50
undirected = True
logging.debug("MeanTheta=" + str(meanTheta))
numReps = 10
numInfectedIndices = []
numRemovedIndices = []
numRemovedEdges = []
numContactEdges = []
statistics = GraphStatistics()
statsTimes = numpy.arange(0, endDate, recordStep)
for i in range(numReps):
graph = HIVGraph(M, undirected)
logging.info("Created graph at index " + str(i) + ": " + 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(endDate)
model.setRecordStep(recordStep)
model.setParams(meanTheta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
vertexArray, infectedIndices, removedIndices, contactGraphStats, removedGraphStats = HIVModelUtils.generateStatistics(
graph, statsTimes
)
numInfectedIndices.append([len(x) for x in infectedIndices])
numRemovedIndices.append([len(x) for x in removedIndices])
numContactEdges.append(contactGraphStats[:, statistics.numVerticesIndex])
numRemovedEdges.append(removedGraphStats[:, statistics.numVerticesIndex])
numInfectedIndices = numpy.array(numInfectedIndices)
numInfectedIndices = numpy.mean(numInfectedIndices, 0)
numRemovedIndices = numpy.array(numRemovedIndices)
numRemovedIndices = numpy.mean(numRemovedIndices, 0)
numContactEdges = numpy.array(numContactEdges)
numContactEdges = numpy.mean(numContactEdges, 0)
numRemovedEdges = numpy.array(numRemovedEdges)
numRemovedEdges = numpy.mean(numRemovedEdges, 0)
return statsTimes, numInfectedIndices, numRemovedIndices, numContactEdges, numRemovedEdges, vertexArray[:, 6]
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):
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 profileSimulate(self):
startDate, endDates, numRecordSteps, M, targetGraph = HIVModelUtils.realSimulationParams()
meanTheta, sigmaTheta = HIVModelUtils.estimatedRealTheta()
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+1000)
model.setRecordStep(10)
model.setParams(meanTheta)
logging.debug("MeanTheta=" + str(meanTheta))
ProfileUtils.profile('model.simulate()', globals(), locals())
numRemoved = numpy.zeros(numRepetitions)
for j in range(numRepetitions):
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)
model.setT(endDate)
model.setRecordStep(recordStep)
model.setParams(theta)
logging.debug("Theta = " + str(theta))
times, infectedIndices, removedIndices, graph = model.simulate(True)
graphFileName = outputDir + "ToyEpidemicGraph" + str(j)
graph.save(graphFileName)
graphList.append(graph)
numInfected[j] = len(graph.getInfectedSet())
numRemoved[j] = len(graph.getRemovedSet())
logging.debug("Infected (mean, std): " + str((numpy.mean(numInfected), numpy.std(numInfected))))
logging.debug("Removed (mean, std): " + str((numpy.mean(numRemoved), numpy.std(numRemoved))))
logging.debug("All done.")
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(endDate)
model.setRecordStep(recordStep)
model.setParams(meanTheta)
logging.debug("MeanTheta=" + str(meanTheta))
times, infectedIndices, removedIndices, graph = model.simulate(True)
statistics = GraphStatistics()
vertexArray, infectedIndices, removedIndices, contactGraphStats, removedGraphStats = HIVModelUtils.generateStatistics(graph, times)
numInfectedIndices = [len(x) for x in infectedIndices]
plt.figure(0)
plt.plot(times, numInfectedIndices)
plt.xlabel("Time")
plt.ylabel("Infected")
