def findDerivative(args):
pertScale, startDate, endDate, recordStep, M, targetGraph, seed = args
numpy.random.seed(seed)
meanTheta, sigmaTheta = HIVModelUtils.toyTheta()
epsilon = 5.0
undirected = True
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
graph = HIVGraph(M, undirected)
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("PATH", alpha=0.5, featureInds=featureInds, useWeightM=False)
abcParams = HIVABCParameters(meanTheta, sigmaTheta, pertScale)
newTheta = abcParams.perturbationKernel(meanTheta)
undirected = True
graph = HIVGraph(M, undirected)
graphMetrics = HIVGraphMetrics2(targetGraph, epsilon, matcher, float(endDate))
graphMetrics.breakDist = 1.0
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
model.setParams(meanTheta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
return abs(0.7 - graphMetrics.distance())/numpy.linalg.norm(newTheta-meanTheta)
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]))
logging.debug("New epsilon array: " + str(abcSMC.epsilonArray))
logging.debug("Number of ABC runs: " + str(abcSMC.numRuns))
logging.debug("All done!")
of times and average the results. The purpose is to test the ABC model selection
by using a known value of theta.
"""
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
numpy.seterr(all='raise')
numpy.random.seed(24)
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
startDate, endDate, recordStep, M = HIVModelUtils.toySimulationParams(False)
endDate += HIVModelUtils.toyTestPeriod
numRepetitions = 1
undirected = True
outputDir = PathDefaults.getOutputDir() + "viroscopy/toy/"
theta, sigmaTheta = HIVModelUtils.toyTheta()
graphList = []
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)
