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)