def saveStats(args):
i, theta = args
resultsFileName = outputDir + "SimStats" + str(i) + ".pkl"
lock = FileLock(resultsFileName)
if not lock.fileExists() and not lock.isLocked():
lock.lock()
model = HIVModelUtils.createModel(targetGraph, startDate, endDate, recordStep, M, matchAlpha, breakSize, matchAlg, theta=thetaArray[i])
times, infectedIndices, removedIndices, graph, compTimes, graphMetrics = HIVModelUtils.simulate(model)
times = numpy.arange(startDate, endDate+1, recordStep)
vertexArray, infectedIndices, removedIndices, contactGraphStats, removedGraphStats, finalRemovedDegrees = HIVModelUtils.generateStatistics(graph, times)
stats = times, vertexArray, infectedIndices, removedGraphStats, finalRemovedDegrees, graphMetrics.objectives, compTimes
Util.savePickle(stats, resultsFileName)
lock.unlock()
else:
logging.debug("Results already computed: " + str(resultsFileName))
thetaArray = loadThetaArray(N, resultsDir, t)[0]
logging.debug(thetaArray)
paramList = []
for i in range(thetaArray.shape[0]):
paramList.append((i, thetaArray[i, :]))
pool = multiprocessing.Pool(multiprocessing.cpu_count())
resultIterator = pool.map(saveStats, paramList)
#resultIterator = map(saveStats, paramList)
pool.terminate()
#Now save the statistics on the target graph
times = numpy.arange(startDate, endDate+1, recordStep)
vertexArray, infectedIndices, removedIndices, contactGraphStats, removedGraphStats, finalRemovedDegrees = HIVModelUtils.generateStatistics(targetGraph, times)
stats = vertexArray, infectedIndices, removedIndices, contactGraphStats, removedGraphStats, finalRemovedDegrees
resultsFileName = outputDir + "IdealStats.pkl"
Util.savePickle(stats, resultsFileName)
else:
import matplotlib
matplotlib.use("GTK3Agg")
import matplotlib.pyplot as plt
plotStyles = ['k-', 'kx-', 'k+-', 'k.-', 'k*-']
N, resultsDir, outputDir, recordStep, startDate, endDate, prefix, targetGraph, breakSize, numEpsilons, M, matchAlpha, matchAlg, numInds = loadParams(0)
inds = range(numInds)
numRecordSteps = int((endDate-startDate)/recordStep)+1
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")
plt.figure(1)
plt.plot(times, contactGraphStats[:, statistics.numVerticesIndex])
plt.xlabel("Time")
plt.ylabel("Contact graph size")
plt.show()
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]
featureInds[HIVVertices.hiddenDegreeIndex] = False
featureInds[HIVVertices.stateIndex] = False
featureInds = numpy.arange(featureInds.shape[0])[featureInds]
matcher = GraphMatch("PATH", alpha=0.5, featureInds=featureInds, useWeightM=False)
graphMetrics = HIVGraphMetrics2(targetGraph, epsilon, matcher, float(endDate))
graphMetrics.breakDist = 1.0
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
model.setParams(meanTheta)
numpy.random.seed(i)
times, infectedIndices, removedIndices, graph = model.simulate(True)
print(times)
times2, vertexArray, removedGraphStats = HIVModelUtils.generateStatistics(graph, startDate, endDate, recordStep)
print(graphMetrics.dists)
graphDists.append(graphMetrics.dists)
removedArray.append(vertexArray[:, 0])
maleArray.append(vertexArray[:, 1])
femaleArray.append(vertexArray[:, 2])
biArray.append(vertexArray[:, 4])
graphDists = numpy.array(graphDists)
removedArray = numpy.array(removedArray)
maleArray = numpy.array(maleArray)
femaleArray = numpy.array(femaleArray)
biArray = numpy.array(biArray)
graphDistsMean = graphDists.mean(0)